analysis_notebooks/analysis_statistical_testing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BZTbIfvIM48x"
      },
      "source": [
        "# Imports\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit_posthocs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJJpGhfwbUZ2",
        "outputId": "7c1a082f-6dde-40e9-c977-312311cb4a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit_posthocs\n",
            "  Downloading scikit_posthocs-0.9.1-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from scikit_posthocs) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from scikit_posthocs) (1.13.1)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (from scikit_posthocs) (0.14.4)\n",
            "Requirement already satisfied: pandas>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from scikit_posthocs) (2.2.2)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from scikit_posthocs) (0.13.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from scikit_posthocs) (3.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->scikit_posthocs) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->scikit_posthocs) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->scikit_posthocs) (2024.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit_posthocs) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit_posthocs) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit_posthocs) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit_posthocs) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit_posthocs) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit_posthocs) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit_posthocs) (3.1.4)\n",
            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels->scikit_posthocs) (0.5.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.6->statsmodels->scikit_posthocs) (1.16.0)\n",
            "Downloading scikit_posthocs-0.9.1-py3-none-any.whl (32 kB)\n",
            "Installing collected packages: scikit_posthocs\n",
            "Successfully installed scikit_posthocs-0.9.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qGkJL5__M48y"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import glob\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "import pylab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L5kGJfQFM48z"
      },
      "outputs": [],
      "source": [
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "# import colorcet as cc\n",
        "import os\n",
        "import plotly.io as pio\n",
        "import scikit_posthocs as sp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H-dTVvxlM48z"
      },
      "outputs": [],
      "source": [
        "from numpy.random import seed\n",
        "from numpy.random import randn\n",
        "from scipy.stats import shapiro\n",
        "from scipy.stats import friedmanchisquare"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# #colab\n",
        "# from google.colab import auth\n",
        "# auth.authenticate_user()\n",
        "# project_id = \"idc-sandbox-003\"\n",
        "# os.environ[\"GCP_PROJECT_ID\"] = project_id\n",
        "# from google.cloud import bigquery\n",
        "# bq_client = bigquery.Client(os.environ[\"GCP_PROJECT_ID\"])"
      ],
      "metadata": {
        "id": "igW36UVGMhuh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# selection_query = f\"\"\"\n",
        "# SELECT *\n",
        "# FROM `idc-sandbox-003.prostate_seg_terra_mhub_v3.final_table_looker_studio`\n",
        "#  \"\"\"\n",
        "# selection_result = bq_client.query(selection_query)\n",
        "# data_df = selection_result.result().to_dataframe()\n",
        "!wget https://raw.githubusercontent.com/ImagingDataCommons/idc-prostate-mri-analysis/refs/heads/main/analysis_results/analysis_results.csv -O analysis_results.csv\n",
        "data_df = pd.read_csv(\"analysis_results.csv\")"
      ],
      "metadata": {
        "id": "LQY_5qxfMogl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "QXiPG1xvM48z"
      },
      "outputs": [],
      "source": [
        "# pd.set_option('display.max_colwidth', -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "x9rI-5CJM48z",
        "outputId": "dc83064e-802b-496a-cc05-74de4bb8957a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>.container { width:100% !important; }</style>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from IPython.display import display, HTML\n",
        "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wcq5dY-VM48z",
        "outputId": "a7153f8c-2257-4446-8ccb-847ef1dc87c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4909 entries, 0 to 4908\n",
            "Data columns (total 62 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   segmentationInstanceUID               4907 non-null   object \n",
            " 1   sourceSegmentedSeriesUID              4907 non-null   object \n",
            " 2   expert_QuantityCodeMeaning            4909 non-null   object \n",
            " 3   expert_QuantityCodeValue              4909 non-null   object \n",
            " 4   expert_Value                          4909 non-null   object \n",
            " 5   expert_UnitsCodeValue                 4909 non-null   object \n",
            " 6   expert_UnitsCodeMeaning               4909 non-null   object \n",
            " 7   expert_findingSiteCodeValue           4909 non-null   object \n",
            " 8   expert_findingSiteCodeMeaning         4909 non-null   object \n",
            " 9   ai_QuantityCodeMeaning                4907 non-null   object \n",
            " 10  ai_QuantityCodeValue                  4907 non-null   object \n",
            " 11  ai_QuantityCodingSchemeDesignator     4907 non-null   object \n",
            " 12  ai_Value                              4907 non-null   object \n",
            " 13  ai_UnitsCodeValue                     4907 non-null   object \n",
            " 14  ai_UnitsCodeMeaning                   4907 non-null   object \n",
            " 15  ai_findingSiteCodeValue               4907 non-null   object \n",
            " 16  ai_findingSiteCodeMeaning             4907 non-null   object \n",
            " 17  StudyInstanceUID                      4909 non-null   object \n",
            " 18  SeriesInstanceUID                     4909 non-null   object \n",
            " 19  SegmentedPropertyCategoryCodeValue    4909 non-null   object \n",
            " 20  SegmentedPropertyCategoryCodeMeaning  4909 non-null   object \n",
            " 21  SegmentedPropertyTypeCodeValue        4909 non-null   object \n",
            " 22  SegmentedPropertyTypeCodeMeaning      4909 non-null   object \n",
            " 23  SegmentAlgorithmType                  4909 non-null   object \n",
            " 24  SegmentNumber                         4909 non-null   Int64  \n",
            " 25  segmented_SeriesInstanceUID           4909 non-null   object \n",
            " 26  expert_measurements_SOPInstanceUID    4909 non-null   object \n",
            " 27  segmentation_SOPInstanceUID           4909 non-null   object \n",
            " 28  refSerieUID                           4909 non-null   object \n",
            " 29  ai_seg_serieUID                       4909 non-null   object \n",
            " 30  idc_seg_serieUID                      4909 non-null   object \n",
            " 31  aiSegmentNumber                       4909 non-null   Int64  \n",
            " 32  idcSegmentNumber                      4909 non-null   Int64  \n",
            " 33  idcSegmentCodeMeaning                 4909 non-null   object \n",
            " 34  aiSegmentCodeMeaning                  4909 non-null   object \n",
            " 35  aiSegmentCodeValue                    4909 non-null   Int64  \n",
            " 36  idcSegmentCodeValue                   4909 non-null   object \n",
            " 37  aiSegmentCodingScheme                 4909 non-null   object \n",
            " 38  idcSegmentCodingScheme                4909 non-null   object \n",
            " 39  dsc                                   4909 non-null   float64\n",
            " 40  hsdff                                 4909 non-null   float64\n",
            " 41  hsdff_95                              4909 non-null   float64\n",
            " 42  tp                                    4909 non-null   Int64  \n",
            " 43  fp                                    4909 non-null   Int64  \n",
            " 44  tn                                    4909 non-null   Int64  \n",
            " 45  fn                                    4909 non-null   Int64  \n",
            " 46  asd                                   4909 non-null   float64\n",
            " 47  collection_id                         4909 non-null   object \n",
            " 48  image_SeriesInstanceUID               4909 non-null   object \n",
            " 49  image_PixelSpacing                    4909 non-null   object \n",
            " 50  image_SliceThickness                  4909 non-null   float64\n",
            " 51  image_MagneticFieldStrength           4909 non-null   object \n",
            " 52  image_SeriesDate                      4909 non-null   dbdate \n",
            " 53  image_StudyDate                       4909 non-null   dbdate \n",
            " 54  image_StudyDescription                4909 non-null   object \n",
            " 55  image_StudyDescription_1              4909 non-null   object \n",
            " 56  image_Manufacturer                    4909 non-null   object \n",
            " 57  image_ManufacturerModelName           4909 non-null   object \n",
            " 58  image_PatientID                       4909 non-null   object \n",
            " 59  SegmentAlgorithmName                  4909 non-null   object \n",
            " 60  ohif_url                              4909 non-null   object \n",
            " 61  algorithmNameCollection               4909 non-null   object \n",
            "dtypes: Int64(8), dbdate(2), float64(5), object(47)\n",
            "memory usage: 2.4+ MB\n"
          ]
        }
      ],
      "source": [
        "data_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "ddrZoMicM48z",
        "outputId": "0d97acd2-b814-415b-cbd8-9350215fb7ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             segmentationInstanceUID  \\\n",
              "0  1.2.276.0.7230010.3.1.4.313263360.645.17286796...   \n",
              "1  1.2.276.0.7230010.3.1.4.313263360.162.17286794...   \n",
              "2  1.2.276.0.7230010.3.1.4.313263360.151.17286799...   \n",
              "3  1.2.276.0.7230010.3.1.4.313263360.150.17286825...   \n",
              "4  1.2.276.0.7230010.3.1.4.313263360.210.17286796...   \n",
              "\n",
              "                            sourceSegmentedSeriesUID  \\\n",
              "0  1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...   \n",
              "1  1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...   \n",
              "2  1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...   \n",
              "3  1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...   \n",
              "4  1.3.6.1.4.1.14519.5.2.1.7311.5101.196511587017...   \n",
              "\n",
              "  expert_QuantityCodeMeaning expert_QuantityCodeValue      expert_Value  \\\n",
              "0             Volume of Mesh                     RNU0   58480.969000000   \n",
              "1             Volume of Mesh                     RNU0   58480.969000000   \n",
              "2             Volume of Mesh                     RNU0   58480.969000000   \n",
              "3             Volume of Mesh                     RNU0   58480.969000000   \n",
              "4             Volume of Mesh                     RNU0  131472.031000000   \n",
              "\n",
              "  expert_UnitsCodeValue expert_UnitsCodeMeaning expert_findingSiteCodeValue  \\\n",
              "0                   mm3        cubic millimeter                    41216001   \n",
              "1                   mm3        cubic millimeter                    41216001   \n",
              "2                   mm3        cubic millimeter                    41216001   \n",
              "3                   mm3        cubic millimeter                    41216001   \n",
              "4                   mm3        cubic millimeter                    41216001   \n",
              "\n",
              "  expert_findingSiteCodeMeaning ai_QuantityCodeMeaning  ... image_SeriesDate  \\\n",
              "0                      Prostate         Volume of Mesh  ...       2011-03-03   \n",
              "1                      Prostate         Volume of Mesh  ...       2011-03-03   \n",
              "2                      Prostate         Volume of Mesh  ...       2011-03-03   \n",
              "3                      Prostate         Volume of Mesh  ...       2011-03-03   \n",
              "4                      Prostate         Volume of Mesh  ...       2012-03-27   \n",
              "\n",
              "  image_StudyDate                         image_StudyDescription  \\\n",
              "0      2011-03-03  MR prostaat kanker detectie WDS_mc MCAPRODETW   \n",
              "1      2011-03-03  MR prostaat kanker detectie WDS_mc MCAPRODETW   \n",
              "2      2011-03-03  MR prostaat kanker detectie WDS_mc MCAPRODETW   \n",
              "3      2011-03-03  MR prostaat kanker detectie WDS_mc MCAPRODETW   \n",
              "4      2012-03-27  MR prostaat kanker detectie WDS_mc MCAPRODETW   \n",
              "\n",
              "                        image_StudyDescription_1 image_Manufacturer  \\\n",
              "0  MR prostaat kanker detectie WDS_mc MCAPRODETW            SIEMENS   \n",
              "1  MR prostaat kanker detectie WDS_mc MCAPRODETW            SIEMENS   \n",
              "2  MR prostaat kanker detectie WDS_mc MCAPRODETW            SIEMENS   \n",
              "3  MR prostaat kanker detectie WDS_mc MCAPRODETW            SIEMENS   \n",
              "4  MR prostaat kanker detectie WDS_mc MCAPRODETW            SIEMENS   \n",
              "\n",
              "  image_ManufacturerModelName image_PatientID          SegmentAlgorithmName  \\\n",
              "0                       Skyra  ProstateX-0293  nnunet_prostate_zonal_task05   \n",
              "1                       Skyra  ProstateX-0293             monai_prostate158   \n",
              "2                       Skyra  ProstateX-0293       bamf_nnunet_mr_prostate   \n",
              "3                       Skyra  ProstateX-0293        nnunet_prostate_task24   \n",
              "4                       Skyra  ProstateX-0161       bamf_nnunet_mr_prostate   \n",
              "\n",
              "                                            ohif_url  \\\n",
              "0  https://fir-idc-prostate-ohif.web.app/viewer/1...   \n",
              "1  https://fir-idc-prostate-ohif.web.app/viewer/1...   \n",
              "2  https://fir-idc-prostate-ohif.web.app/viewer/1...   \n",
              "3  https://fir-idc-prostate-ohif.web.app/viewer/1...   \n",
              "4  https://fir-idc-prostate-ohif.web.app/viewer/1...   \n",
              "\n",
              "                  algorithmNameCollection  \n",
              "0  nnunet_prostate_zonal_task05-prostatex  \n",
              "1             monai_prostate158-prostatex  \n",
              "2       bamf_nnunet_mr_prostate-prostatex  \n",
              "3        nnunet_prostate_task24-prostatex  \n",
              "4       bamf_nnunet_mr_prostate-prostatex  \n",
              "\n",
              "[5 rows x 62 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbf0d6e9-f6dc-4a98-ab16-a3a93450e72a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>segmentationInstanceUID</th>\n",
              "      <th>sourceSegmentedSeriesUID</th>\n",
              "      <th>expert_QuantityCodeMeaning</th>\n",
              "      <th>expert_QuantityCodeValue</th>\n",
              "      <th>expert_Value</th>\n",
              "      <th>expert_UnitsCodeValue</th>\n",
              "      <th>expert_UnitsCodeMeaning</th>\n",
              "      <th>expert_findingSiteCodeValue</th>\n",
              "      <th>expert_findingSiteCodeMeaning</th>\n",
              "      <th>ai_QuantityCodeMeaning</th>\n",
              "      <th>...</th>\n",
              "      <th>image_SeriesDate</th>\n",
              "      <th>image_StudyDate</th>\n",
              "      <th>image_StudyDescription</th>\n",
              "      <th>image_StudyDescription_1</th>\n",
              "      <th>image_Manufacturer</th>\n",
              "      <th>image_ManufacturerModelName</th>\n",
              "      <th>image_PatientID</th>\n",
              "      <th>SegmentAlgorithmName</th>\n",
              "      <th>ohif_url</th>\n",
              "      <th>algorithmNameCollection</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.2.276.0.7230010.3.1.4.313263360.645.17286796...</td>\n",
              "      <td>1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>RNU0</td>\n",
              "      <td>58480.969000000</td>\n",
              "      <td>mm3</td>\n",
              "      <td>cubic millimeter</td>\n",
              "      <td>41216001</td>\n",
              "      <td>Prostate</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>...</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>SIEMENS</td>\n",
              "      <td>Skyra</td>\n",
              "      <td>ProstateX-0293</td>\n",
              "      <td>nnunet_prostate_zonal_task05</td>\n",
              "      <td>https://fir-idc-prostate-ohif.web.app/viewer/1...</td>\n",
              "      <td>nnunet_prostate_zonal_task05-prostatex</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.2.276.0.7230010.3.1.4.313263360.162.17286794...</td>\n",
              "      <td>1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>RNU0</td>\n",
              "      <td>58480.969000000</td>\n",
              "      <td>mm3</td>\n",
              "      <td>cubic millimeter</td>\n",
              "      <td>41216001</td>\n",
              "      <td>Prostate</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>...</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>SIEMENS</td>\n",
              "      <td>Skyra</td>\n",
              "      <td>ProstateX-0293</td>\n",
              "      <td>monai_prostate158</td>\n",
              "      <td>https://fir-idc-prostate-ohif.web.app/viewer/1...</td>\n",
              "      <td>monai_prostate158-prostatex</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.2.276.0.7230010.3.1.4.313263360.151.17286799...</td>\n",
              "      <td>1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>RNU0</td>\n",
              "      <td>58480.969000000</td>\n",
              "      <td>mm3</td>\n",
              "      <td>cubic millimeter</td>\n",
              "      <td>41216001</td>\n",
              "      <td>Prostate</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>...</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>SIEMENS</td>\n",
              "      <td>Skyra</td>\n",
              "      <td>ProstateX-0293</td>\n",
              "      <td>bamf_nnunet_mr_prostate</td>\n",
              "      <td>https://fir-idc-prostate-ohif.web.app/viewer/1...</td>\n",
              "      <td>bamf_nnunet_mr_prostate-prostatex</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.2.276.0.7230010.3.1.4.313263360.150.17286825...</td>\n",
              "      <td>1.3.6.1.4.1.14519.5.2.1.7310.5101.460054567474...</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>RNU0</td>\n",
              "      <td>58480.969000000</td>\n",
              "      <td>mm3</td>\n",
              "      <td>cubic millimeter</td>\n",
              "      <td>41216001</td>\n",
              "      <td>Prostate</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>...</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>2011-03-03</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>SIEMENS</td>\n",
              "      <td>Skyra</td>\n",
              "      <td>ProstateX-0293</td>\n",
              "      <td>nnunet_prostate_task24</td>\n",
              "      <td>https://fir-idc-prostate-ohif.web.app/viewer/1...</td>\n",
              "      <td>nnunet_prostate_task24-prostatex</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.2.276.0.7230010.3.1.4.313263360.210.17286796...</td>\n",
              "      <td>1.3.6.1.4.1.14519.5.2.1.7311.5101.196511587017...</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>RNU0</td>\n",
              "      <td>131472.031000000</td>\n",
              "      <td>mm3</td>\n",
              "      <td>cubic millimeter</td>\n",
              "      <td>41216001</td>\n",
              "      <td>Prostate</td>\n",
              "      <td>Volume of Mesh</td>\n",
              "      <td>...</td>\n",
              "      <td>2012-03-27</td>\n",
              "      <td>2012-03-27</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>MR prostaat kanker detectie WDS_mc MCAPRODETW</td>\n",
              "      <td>SIEMENS</td>\n",
              "      <td>Skyra</td>\n",
              "      <td>ProstateX-0161</td>\n",
              "      <td>bamf_nnunet_mr_prostate</td>\n",
              "      <td>https://fir-idc-prostate-ohif.web.app/viewer/1...</td>\n",
              "      <td>bamf_nnunet_mr_prostate-prostatex</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 62 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbf0d6e9-f6dc-4a98-ab16-a3a93450e72a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dbf0d6e9-f6dc-4a98-ab16-a3a93450e72a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dbf0d6e9-f6dc-4a98-ab16-a3a93450e72a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-74aad6de-61cf-42c3-9742-0e928405f381\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-74aad6de-61cf-42c3-9742-0e928405f381')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-74aad6de-61cf-42c3-9742-0e928405f381 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_df"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "data_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oAVhMj9M480"
      },
      "source": [
        "# Analysis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUj3IuAdM480"
      },
      "source": [
        "## Seaborn parameters\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NRjvfCe8M480"
      },
      "outputs": [],
      "source": [
        "sns.set(rc={'figure.figsize':(10,10)})\n",
        "# Setting the font size by 2.5\n",
        "sns.set(font_scale=1.5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JYjP10O2M480"
      },
      "source": [
        "## Mean and STDs of quantitative region and distance based metrics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jb9rkqFnM480",
        "outputId": "46f91b46-d691-4479-c3ac-3496eab9715d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['nnunet_prostate_zonal_task05-prostatex',\n",
              "       'monai_prostate158-prostatex', 'bamf_nnunet_mr_prostate-prostatex',\n",
              "       'nnunet_prostate_task24-prostatex',\n",
              "       'bamf_nnunet_mr_prostate-prostate_mri_us_biopsy',\n",
              "       'nnunet_prostate_task24-prostate_mri_us_biopsy',\n",
              "       'nnunet_prostate_zonal_task05-prostate_mri_us_biopsy',\n",
              "       'monai_prostate158-prostate_mri_us_biopsy',\n",
              "       'bamf_nnunet_mr_prostate-qin_prostate_repeatability',\n",
              "       'nnunet_prostate_zonal_task05-qin_prostate_repeatability',\n",
              "       'monai_prostate158-qin_prostate_repeatability',\n",
              "       'nnunet_prostate_task24-qin_prostate_repeatability'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "data_df.algorithmNameCollection.unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FT1tn8MfM480"
      },
      "source": [
        "### ProstateX\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27bTPhLvM480"
      },
      "source": [
        "#### Whole prostate\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4QHFgZmM480",
        "outputId": "87dd5851-eb83-4c9e-c52c-2597f5a6f662"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 392 entries, 0 to 3553\n",
            "Data columns (total 62 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   segmentationInstanceUID               392 non-null    object \n",
            " 1   sourceSegmentedSeriesUID              392 non-null    object \n",
            " 2   expert_QuantityCodeMeaning            392 non-null    object \n",
            " 3   expert_QuantityCodeValue              392 non-null    object \n",
            " 4   expert_Value                          392 non-null    object \n",
            " 5   expert_UnitsCodeValue                 392 non-null    object \n",
            " 6   expert_UnitsCodeMeaning               392 non-null    object \n",
            " 7   expert_findingSiteCodeValue           392 non-null    object \n",
            " 8   expert_findingSiteCodeMeaning         392 non-null    object \n",
            " 9   ai_QuantityCodeMeaning                392 non-null    object \n",
            " 10  ai_QuantityCodeValue                  392 non-null    object \n",
            " 11  ai_QuantityCodingSchemeDesignator     392 non-null    object \n",
            " 12  ai_Value                              392 non-null    object \n",
            " 13  ai_UnitsCodeValue                     392 non-null    object \n",
            " 14  ai_UnitsCodeMeaning                   392 non-null    object \n",
            " 15  ai_findingSiteCodeValue               392 non-null    object \n",
            " 16  ai_findingSiteCodeMeaning             392 non-null    object \n",
            " 17  StudyInstanceUID                      392 non-null    object \n",
            " 18  SeriesInstanceUID                     392 non-null    object \n",
            " 19  SegmentedPropertyCategoryCodeValue    392 non-null    object \n",
            " 20  SegmentedPropertyCategoryCodeMeaning  392 non-null    object \n",
            " 21  SegmentedPropertyTypeCodeValue        392 non-null    object \n",
            " 22  SegmentedPropertyTypeCodeMeaning      392 non-null    object \n",
            " 23  SegmentAlgorithmType                  392 non-null    object \n",
            " 24  SegmentNumber                         392 non-null    Int64  \n",
            " 25  segmented_SeriesInstanceUID           392 non-null    object \n",
            " 26  expert_measurements_SOPInstanceUID    392 non-null    object \n",
            " 27  segmentation_SOPInstanceUID           392 non-null    object \n",
            " 28  refSerieUID                           392 non-null    object \n",
            " 29  ai_seg_serieUID                       392 non-null    object \n",
            " 30  idc_seg_serieUID                      392 non-null    object \n",
            " 31  aiSegmentNumber                       392 non-null    Int64  \n",
            " 32  idcSegmentNumber                      392 non-null    Int64  \n",
            " 33  idcSegmentCodeMeaning                 392 non-null    object \n",
            " 34  aiSegmentCodeMeaning                  392 non-null    object \n",
            " 35  aiSegmentCodeValue                    392 non-null    Int64  \n",
            " 36  idcSegmentCodeValue                   392 non-null    object \n",
            " 37  aiSegmentCodingScheme                 392 non-null    object \n",
            " 38  idcSegmentCodingScheme                392 non-null    object \n",
            " 39  dsc                                   392 non-null    float64\n",
            " 40  hsdff                                 392 non-null    float64\n",
            " 41  hsdff_95                              392 non-null    float64\n",
            " 42  tp                                    392 non-null    Int64  \n",
            " 43  fp                                    392 non-null    Int64  \n",
            " 44  tn                                    392 non-null    Int64  \n",
            " 45  fn                                    392 non-null    Int64  \n",
            " 46  asd                                   392 non-null    float64\n",
            " 47  collection_id                         392 non-null    object \n",
            " 48  image_SeriesInstanceUID               392 non-null    object \n",
            " 49  image_PixelSpacing                    392 non-null    object \n",
            " 50  image_SliceThickness                  392 non-null    float64\n",
            " 51  image_MagneticFieldStrength           392 non-null    object \n",
            " 52  image_SeriesDate                      392 non-null    dbdate \n",
            " 53  image_StudyDate                       392 non-null    dbdate \n",
            " 54  image_StudyDescription                392 non-null    object \n",
            " 55  image_StudyDescription_1              392 non-null    object \n",
            " 56  image_Manufacturer                    392 non-null    object \n",
            " 57  image_ManufacturerModelName           392 non-null    object \n",
            " 58  image_PatientID                       392 non-null    object \n",
            " 59  SegmentAlgorithmName                  392 non-null    object \n",
            " 60  ohif_url                              392 non-null    object \n",
            " 61  algorithmNameCollection               392 non-null    object \n",
            "dtypes: Int64(8), dbdate(2), float64(5), object(47)\n",
            "memory usage: 196.0+ KB\n"
          ]
        }
      ],
      "source": [
        "prostate_prostatex_df = data_df[(data_df.collection_id == 'prostatex') & (data_df.SegmentedPropertyTypeCodeMeaning.isin(['Prostate', 'Prostatic structure']))]\n",
        "len(prostate_prostatex_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "8qbAKBl7M480",
        "outputId": "f6ba987b-fce6-4d54-d403-517ef06f85bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7cc9c4cbf4c0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1048.37x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.displot(prostate_prostatex_df,  x='dsc', hue=\"algorithmNameCollection\", kind=\"kde\", multiple=\"stack\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T4ZnIBilM480"
      },
      "outputs": [],
      "source": [
        "nnnunet_task024_prostate_df = prostate_prostatex_df[prostate_prostatex_df.algorithmNameCollection == 'nnunet_prostate_task24-prostatex']\n",
        "nnnunet_task05_prostate_df = prostate_prostatex_df[prostate_prostatex_df.algorithmNameCollection == 'nnunet_prostate_zonal_task05-prostatex']\n",
        "monai_prostate158_prostate_df = prostate_prostatex_df[prostate_prostatex_df.algorithmNameCollection == 'monai_prostate158-prostatex']\n",
        "bamf_nnunet_mr_prostate_df = prostate_prostatex_df[prostate_prostatex_df.algorithmNameCollection == 'bamf_nnunet_mr_prostate-prostatex']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert sorted(nnnunet_task024_prostate_df.refSerieUID.unique()) == sorted(nnnunet_task05_prostate_df.refSerieUID.unique()) == sorted(monai_prostate158_prostate_df.refSerieUID.unique()) == sorted(bamf_nnunet_mr_prostate_df.refSerieUID.unique())"
      ],
      "metadata": {
        "id": "GM0EV0B4joqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(nnnunet_task024_prostate_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L871-N_x-60-",
        "outputId": "5938e826-7c71-4aea-c476-0765048c8387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Fm4-t0HDM480",
        "outputId": "aa92565c-a195-473f-b021-c26d0b7d593b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc        asd      hsdff\n",
              "count  98.000000  98.000000  98.000000\n",
              "mean    0.925558   0.510102   6.895405\n",
              "std     0.028936   0.424957   3.621366\n",
              "min     0.740932   0.000000   3.000000\n",
              "25%     0.914818   0.000000   4.690416\n",
              "50%     0.932957   0.530000   6.000000\n",
              "75%     0.942233   0.835000   6.991048\n",
              "max     0.959794   1.350000  26.196373"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.925558</td>\n",
              "      <td>0.510102</td>\n",
              "      <td>6.895405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.028936</td>\n",
              "      <td>0.424957</td>\n",
              "      <td>3.621366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.740932</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.914818</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.690416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.932957</td>\n",
              "      <td>0.530000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.942233</td>\n",
              "      <td>0.835000</td>\n",
              "      <td>6.991048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.959794</td>\n",
              "      <td>1.350000</td>\n",
              "      <td>26.196373</td>\n",
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              "        document.querySelector('#df-42212617-4c51-4977-9c31-1480d0826f5b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-42212617-4c51-4977-9c31-1480d0826f5b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-e88c8361-3de9-4aac-ae61-858c46638801 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnnunet_task024_prostate_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.374634275859364,\n        \"min\": 0.0289358628683878,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9255584285714286,\n          0.9329575,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.466649716922234,\n        \"min\": 0.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          98.0,\n          0.5101020408163266,\n          0.835\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.61977943989452,\n        \"min\": 3.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.895405275510204,\n          6.0,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "nnnunet_task024_prostate_df[['dsc', 'asd', 'hsdff']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_9qAkTEjM480",
        "outputId": "261c8272-3f11-483e-b432-92e38385a1f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  98.000000  98.0  98.000000\n",
              "mean    0.895606   0.0  12.738488\n",
              "std     0.042162   0.0  14.181561\n",
              "min     0.636936   0.0   4.242640\n",
              "25%     0.881353   0.0   6.041523\n",
              "50%     0.902995   0.0   7.424387\n",
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              "      <td>0.0</td>\n",
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              "    </tr>\n",
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              "      <td>0.636936</td>\n",
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              "\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-55c73b96-6ce6-4974-a6ab-3957a6b2ccb4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-55c73b96-6ce6-4974-a6ab-3957a6b2ccb4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-961e0923-ca11-4ab7-97f0-a204fc0a4a04\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-961e0923-ca11-4ab7-97f0-a204fc0a4a04')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-961e0923-ca11-4ab7-97f0-a204fc0a4a04 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnnunet_task05_prostate_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.38544894519213,\n        \"min\": 0.04216150367945911,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8956061122448977,\n          0.902995,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.64823227814083,\n        \"min\": 0.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.08781498709611,\n        \"min\": 4.24264,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.7384876122449,\n          7.4243875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "nnnunet_task05_prostate_df[['dsc', 'asd', 'hsdff']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "HP5jpxeBM480",
        "outputId": "28703f73-bce9-40f0-c9e5-ed822143935d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  98.000000  98.0  98.000000\n",
              "mean    0.919073   0.0   6.940947\n",
              "std     0.018347   0.0   2.465612\n",
              "min     0.860250   0.0   3.535534\n",
              "25%     0.910644   0.0   5.830952\n",
              "50%     0.920005   0.0   6.103278\n",
              "75%     0.930457   0.0   8.111298\n",
              "max     0.956414   0.0  19.685020"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cfb552a6-46a7-4c31-bd3a-ffeb0060386f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.919073</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.940947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.018347</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.465612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.860250</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.535534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.910644</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.830952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.920005</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.103278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.930457</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.111298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.956414</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.685020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfb552a6-46a7-4c31-bd3a-ffeb0060386f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cfb552a6-46a7-4c31-bd3a-ffeb0060386f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cfb552a6-46a7-4c31-bd3a-ffeb0060386f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4e5295b1-ee59-4b99-bf41-a0f9f7190449\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e5295b1-ee59-4b99-bf41-a0f9f7190449')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4e5295b1-ee59-4b99-bf41-a0f9f7190449 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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              "summary": "{\n  \"name\": \"monai_prostate158_prostate_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.3711193280801,\n        \"min\": 0.018347014928556084,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9190725000000001,\n          0.9200045,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.64823227814083,\n        \"min\": 0.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.420272708572696,\n        \"min\": 2.4656117883907434,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.940946826530611,\n          6.103278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "monai_prostate158_prostate_df[['dsc', 'asd', 'hsdff']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bamf_nnunet_mr_prostate_df[['dsc', 'asd', 'hsdff']].describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Idr1gJ6F_U8n",
        "outputId": "aca360a0-35d5-42ac-ea37-9a9c55e12ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc        asd      hsdff\n",
              "count  98.000000  98.000000  98.000000\n",
              "mean    0.952376   0.261939   4.679789\n",
              "std     0.011993   0.232976   2.678833\n",
              "min     0.905998   0.000000   3.000000\n",
              "25%     0.946287   0.000000   3.162278\n",
              "50%     0.953210   0.280000   3.552776\n",
              "75%     0.960622   0.397500   5.074265\n",
              "max     0.973736   1.010000  18.466185"
            ],
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              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.952376</td>\n",
              "      <td>0.261939</td>\n",
              "      <td>4.679789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.011993</td>\n",
              "      <td>0.232976</td>\n",
              "      <td>2.678833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.905998</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.280000</td>\n",
              "      <td>3.552776</td>\n",
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              "      <td>0.960622</td>\n",
              "      <td>0.397500</td>\n",
              "      <td>5.074265</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"bamf_nnunet_mr_prostate_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.36169443897979,\n        \"min\": 0.011992790911271183,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9523757448979593,\n          0.9532095,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.53944923918001,\n        \"min\": 0.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          98.0,\n          0.2619387755102041,\n          0.3975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.01452599871674,\n        \"min\": 2.67883254808522,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.67978869387755,\n          3.5527755,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8wnzl2DVM481"
      },
      "source": [
        "Normality tests\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKaDCbh5M481",
        "outputId": "85674a8a-3075-4b90-fa32-416989550f98"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Statistics=0.764, p=0.000\n",
            "Sample does not look Gaussian (reject H0)\n",
            "\n",
            "\n",
            "Statistics=0.726, p=0.000\n",
            "Sample does not look Gaussian (reject H0)\n",
            "\n",
            "\n",
            "Statistics=0.969, p=0.022\n",
            "Sample does not look Gaussian (reject H0)\n",
            "\n",
            "\n",
            "Statistics=0.952, p=0.001\n",
            "Sample does not look Gaussian (reject H0)\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for df_val in [nnnunet_task024_prostate_df,\n",
        "               nnnunet_task05_prostate_df,\n",
        "               monai_prostate158_prostate_df,\n",
        "               bamf_nnunet_mr_prostate_df]:\n",
        "    # normality test\n",
        "    stat, p = shapiro(df_val.dsc.values)\n",
        "    print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
        "    # interpret\n",
        "    alpha = 0.05\n",
        "    if p > alpha:\n",
        "        print('Sample looks Gaussian (fail to reject H0)')\n",
        "    else:\n",
        "        print('Sample does not look Gaussian (reject H0)')\n",
        "    print(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ygZ_NT_yM481"
      },
      "outputs": [],
      "source": [
        "# sns.histplot(data=monai_prostate158_prostate_df, x=\"dsc\")\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8D3cmvNxM481",
        "outputId": "3076966c-24df-4a56-8a15-7bd4a8ed398c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value for normality: 3.033528584386632e-11\n"
          ]
        }
      ],
      "source": [
        "# Checking normality\n",
        "_, p_normality = stats.shapiro(nnnunet_task024_prostate_df['dsc'])\n",
        "print(f'p-value for normality: {p_normality}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v82wklSjM481"
      },
      "source": [
        "Mean and STD deviations for prostatex whole prostate results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8th-A9GM481",
        "outputId": "47eda4a2-0063-4e05-aec9-4d71fd439160"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nnunet task024 model DSC mean and std for whole prostate: 0.93/0.03\n",
            "nnunet task05 model DSC mean and std for whole prostate: 0.9/0.04\n",
            "nnunet monai prostate158 DSC model mean and std for whole prostate: 0.92/0.02\n",
            "nnunet monai prostate158 DSC model mean and std for whole prostate: 0.95/0.01\n"
          ]
        }
      ],
      "source": [
        "print(f\"nnunet task024 model DSC mean and std for whole prostate: {round(np.mean(nnnunet_task024_prostate_df.dsc.values),2)}/{round(np.std(nnnunet_task024_prostate_df.dsc.values),2)}\")\n",
        "print(f\"nnunet task05 model DSC mean and std for whole prostate: {round(np.mean(nnnunet_task05_prostate_df.dsc.values),2)}/{round(np.std(nnnunet_task05_prostate_df.dsc.values),2)}\")\n",
        "print(f\"nnunet monai prostate158 DSC model mean and std for whole prostate: {round(np.mean(monai_prostate158_prostate_df.dsc.values),2)}/{round(np.std(monai_prostate158_prostate_df.dsc.values),2)}\")\n",
        "print(f\"nnunet monai prostate158 DSC model mean and std for whole prostate: {round(np.mean(bamf_nnunet_mr_prostate_df.dsc.values),2)}/{round(np.std(bamf_nnunet_mr_prostate_df.dsc.values),2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mNAgEAKM481",
        "outputId": "b037a5d0-2b32-4f06-d8ca-442e968b2148"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "227.84081632653056\n",
            "4.0517712834878306e-49\n"
          ]
        }
      ],
      "source": [
        "res = friedmanchisquare(nnnunet_task024_prostate_df.sort_values(by=[\"refSerieUID\"]).dsc.values,\n",
        "                        nnnunet_task05_prostate_df.sort_values(by=[\"refSerieUID\"]).dsc.values,\n",
        "                        monai_prostate158_prostate_df.sort_values(by=[\"refSerieUID\"]).dsc.values,\n",
        "                        bamf_nnunet_mr_prostate_df.sort_values(by=[\"refSerieUID\"]).dsc.values)\n",
        "print(res.statistic)\n",
        "print(res.pvalue)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "l-g9ZyweM481",
        "outputId": "d3f631a8-f7d7-463f-8763-0acd460a27a6"
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      "outputs": [
        {
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          "data": {
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              "                                        bamf_nnunet_mr_prostate-prostatex  \\\n",
              "bamf_nnunet_mr_prostate-prostatex                            1.000000e+00   \n",
              "monai_prostate158-prostatex                                  2.024997e-37   \n",
              "nnunet_prostate_task24-prostatex                             1.138467e-22   \n",
              "nnunet_prostate_zonal_task05-prostatex                       4.875891e-60   \n",
              "\n",
              "                                        monai_prostate158-prostatex  \\\n",
              "bamf_nnunet_mr_prostate-prostatex                      2.024997e-37   \n",
              "monai_prostate158-prostatex                            1.000000e+00   \n",
              "nnunet_prostate_task24-prostatex                       1.027319e-03   \n",
              "nnunet_prostate_zonal_task05-prostatex                 9.261635e-07   \n",
              "\n",
              "                                        nnunet_prostate_task24-prostatex  \\\n",
              "bamf_nnunet_mr_prostate-prostatex                           1.138467e-22   \n",
              "monai_prostate158-prostatex                                 1.027319e-03   \n",
              "nnunet_prostate_task24-prostatex                            1.000000e+00   \n",
              "nnunet_prostate_zonal_task05-prostatex                      2.117918e-17   \n",
              "\n",
              "                                        nnunet_prostate_zonal_task05-prostatex  \n",
              "bamf_nnunet_mr_prostate-prostatex                                 4.875891e-60  \n",
              "monai_prostate158-prostatex                                       9.261635e-07  \n",
              "nnunet_prostate_task24-prostatex                                  2.117918e-17  \n",
              "nnunet_prostate_zonal_task05-prostatex                            1.000000e+00  "
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              "      <th>bamf_nnunet_mr_prostate-prostatex</th>\n",
              "      <th>monai_prostate158-prostatex</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"sp\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"bamf_nnunet_mr_prostate-prostatex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5,\n        \"min\": 4.875891291069216e-60,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.024997010899783e-37,\n          4.875891291069216e-60,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"monai_prostate158-prostatex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4998288602453174,\n        \"min\": 2.024997010899783e-37,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          9.261634529155409e-07,\n          2.024997010899783e-37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nnunet_prostate_task24-prostatex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49982901481713893,\n        \"min\": 1.138467029572332e-22,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0010273187573138448,\n          2.1179175017964847e-17,\n          1.138467029572332e-22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nnunet_prostate_zonal_task05-prostatex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49999984563961514,\n        \"min\": 4.875891291069216e-60,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.261634529155409e-07,\n          1.0,\n          4.875891291069216e-60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "sp.posthoc_conover(prostate_prostatex_df, val_col='dsc', group_col='algorithmNameCollection', p_adjust = 'bonf')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Zhua7GPM481"
      },
      "source": [
        "Conover pairwise post-hoc test tells us that there are significant differences across all models, especially task05 and the rest of the models.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGmRqt5PM481"
      },
      "source": [
        "Using a significance level of 5%, we would reject the null hypothesis in favor of the alternative hypothesis: “Dice scores are different across models for ProstateX collections”.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nHYJH5eM481"
      },
      "source": [
        "Friedman test tells us that there are significant differences(x2(3)=54, p=0.12e-13) in dice scores between all 3 models for ProstateX collection.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_ZPUnm8M482"
      },
      "source": [
        "#### Paired tests on mean DSC, same collection == Wilcoxon signed rank test with Bon.. correction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAO5SFerM482"
      },
      "source": [
        "The Wilcoxon signed-rank test tests the null hypothesis that two related paired samples come from the same distribution.\n",
        "\n",
        "In particular, it tests whether the distribution of the differences x - y is symmetric about zero.\n",
        "\n",
        "It is a non-parametric version of the paired T-test.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00VO-ditM482"
      },
      "source": [
        "The p-value measures the probability of getting a more extreme value than the one you got from the experiment.\n",
        "\n",
        "If the p-value is greater than alpha, you accept the null hypothesis.\n",
        "\n",
        "If it is less than alpha, you reject the null hypothesis.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uioRcp4dM482"
      },
      "source": [
        "Other infos on statistical testing :\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vG0ddZcfM482"
      },
      "outputs": [],
      "source": [
        "from scipy.stats import wilcoxon"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSRtgFFRM482"
      },
      "source": [
        "**Bonferroni correction : 4 paired Wilcoxon tests so alpha = 0.05/4 = 0.0125**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFDLFheqM482"
      },
      "source": [
        "Pair 1 : task024 dsc differences vs task05\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WzwjXck-M482"
      },
      "outputs": [],
      "source": [
        "diff = nnnunet_task024_prostate_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_df.sort_values(by=['refSerieUID']).dsc.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_NGDsXFM482",
        "outputId": "491620f7-1155-40c8-eb0e-d6f4f2e84a88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(85.0, 1.0974039719164549e-16)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jm2Qk7dvM482",
        "outputId": "e2856af2-1d19-499a-a1cf-15215e58f272"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4766.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='less')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZyRazX0uM482"
      },
      "source": [
        "This shows that the null hypothesis that the median of dsc differences is negative can be rejected at a confidence level of 5% in favor of the alternative that the median is greater than zero.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54RRzT1YM482"
      },
      "source": [
        "Pair 2 : task024 dsc differences vs monai\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jW5zr3WTM482",
        "outputId": "74d71b03-7741-4eb8-8764-5ff878526ef5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1317.0, 8.56355971010145e-05)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "diff = nnnunet_task024_prostate_df.sort_values(by=['refSerieUID']).dsc.values - monai_prostate158_prostate_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9xZ7Jz0M482",
        "outputId": "e973760f-bd95-4715-99a7-f9746f76cd0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3534.0, 0.9999571822014495)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='less')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qFaOqf7M482"
      },
      "source": [
        "Pair 3 : task05 dsc differences vs monai\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32Q1ONUxM482",
        "outputId": "2d49cb1d-51b2-448e-a4b9-ae0aec7610cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(446.0, 2.3075515740758437e-12)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "diff = monai_prostate158_prostate_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4g2qNd8CM482",
        "outputId": "601540ba-1fa0-4317-842d-7d9d85a3e03e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4405.0, 1.1537757870379218e-12)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 4 : bamf_nnunet_mr_prostate vs nnunet_task024"
      ],
      "metadata": {
        "id": "g68jgJW9CfKU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23d241b1-9796-4ef7-ec51-ca2d8756e4f6",
        "id": "H3cqkAqfCu5G"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 8.860504485789389e-18)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task024_prostate_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48a6ef6a-1ce7-4a16-8549-2f521795174e",
        "id": "jmoWhaFeCu5P"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4849.0, 4.4302522428946944e-18)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 5 : bamf_nnunet_mr_prostate vs nnunet_task05"
      ],
      "metadata": {
        "id": "bM79iuvXCnEa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ab71e27-ac9b-4821-d7d2-43892a22f83a",
        "id": "b5muefc6CvfL"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 8.33039747348591e-18)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13604c42-58f5-4fe8-ccb7-0d2ac47a4e80",
        "id": "GKtgHH3ECvfM"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4851.0, 4.165198736742955e-18)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 6 : bamf_nnunet_mr_prostate vs monai prostate158"
      ],
      "metadata": {
        "id": "F0FAP84YCpmB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2424c201-48fd-459f-8964-68e2857ed0c7",
        "id": "acJGMw1zCv6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3.0, 9.137907036390102e-18)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_df.sort_values(by=['refSerieUID']).dsc.values - monai_prostate158_prostate_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be7358a3-a44c-4527-a03e-ad8121c58342",
        "id": "NgIKDA_xCv6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4848.0, 4.568953518195051e-18)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7WX4KxcM483"
      },
      "source": [
        "#### Peripheral Zone\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.SegmentedPropertyTypeCodeMeaning.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-X8zNinHshq",
        "outputId": "aee4e5d6-39e8-4bdd-84af-25a55ada9879"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Prostate', 'Prostatic structure',\n",
              "       'Structure of peripheral glandular zone of prostate (body structure)',\n",
              "       'Structure of transition zone of prostate (body structure)'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-1qUW_hVM483",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5bf8bb2-71c7-4e38-9dea-a8799a84ebca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "195"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "pz_prostatex_df = data_df[(data_df.collection_id == 'prostatex') & (data_df.SegmentedPropertyTypeCodeMeaning == 'Structure of peripheral glandular zone of prostate (body structure)')]\n",
        "len(pz_prostatex_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BbS8tQ4M483",
        "outputId": "f8ccc4cb-3d06-41f1-d54e-3dbb319bfe7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['nnunet_prostate_zonal_task05-prostatex',\n",
              "       'monai_prostate158-prostatex'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "pz_prostatex_df.algorithmNameCollection.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ArDDSBtZM483"
      },
      "outputs": [],
      "source": [
        "nnunet_pz_prostatex_df =  pz_prostatex_df[pz_prostatex_df.algorithmNameCollection == 'nnunet_prostate_zonal_task05-prostatex']\n",
        "monai_pz_prostatex_df = pz_prostatex_df[pz_prostatex_df.algorithmNameCollection == 'monai_prostate158-prostatex']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(nnunet_pz_prostatex_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Y616MO3Iovm",
        "outputId": "95a9e546-ff77-40bf-d548-fed8c0e11dab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(monai_pz_prostatex_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6B3lpRxKIqwK",
        "outputId": "9afc9962-c719-4a1c-9be0-63e8addc7284"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X5ZZ6Mi5M483"
      },
      "outputs": [],
      "source": [
        "# nnunet_pz_prostatex_df = nnunet_pz_prostatex_df.drop_duplicates(subset='refSerieUID', keep=\"last\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNoBgKEEM483",
        "outputId": "bae26474-e50c-4ecf-b98e-d17596177865"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nnunet task05 model DSC mean and std for pz: 0.67/0.14\n",
            "monai model DSC mean and std for pz: 0.74/0.08\n"
          ]
        }
      ],
      "source": [
        "print(f\"nnunet task05 model DSC mean and std for pz: {round(np.mean(nnunet_pz_prostatex_df.dsc.values),2)}/{round(np.std(nnunet_pz_prostatex_df.dsc.values),2)}\")\n",
        "print(f\"monai model DSC mean and std for pz: {round(np.mean(monai_pz_prostatex_df.dsc.values),2)}/{round(np.std(monai_pz_prostatex_df.dsc.values),2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3SQHCTnDM483",
        "outputId": "2e578a36-78f5-44af-ac0f-f2ac1a99682f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(737.0, 3.65036478520205e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "diff = monai_pz_prostatex_df[monai_pz_prostatex_df.refSerieUID.isin(nnunet_pz_prostatex_df.refSerieUID.unique())].sort_values(by=['refSerieUID']).dsc.values - nnunet_pz_prostatex_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jheJw84BM483",
        "outputId": "4b72b1a4-51b8-4a34-ad08-5014637c7d35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4016.0, 1.825182392601025e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nnunet_pz_prostatex_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "MDR6622-0uEz",
        "outputId": "5dde4254-69f3-4d95-aa7c-b24c6ecc5b35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc        asd      hsdff\n",
              "count  97.000000  97.000000  97.000000\n",
              "mean    0.673065   2.006186  16.862658\n",
              "std     0.138787   2.178992   7.145075\n",
              "min     0.059166   0.620000   7.632169\n",
              "25%     0.635312   1.010000  11.264053\n",
              "50%     0.712014   1.360000  15.378556\n",
              "75%     0.755970   2.280000  20.413403\n",
              "max     0.844456  15.620000  43.934612"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ed25ef2-955a-428f-9887-32a16de026a1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>97.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>97.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.673065</td>\n",
              "      <td>2.006186</td>\n",
              "      <td>16.862658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.138787</td>\n",
              "      <td>2.178992</td>\n",
              "      <td>7.145075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.059166</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>7.632169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.635312</td>\n",
              "      <td>1.010000</td>\n",
              "      <td>11.264053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.712014</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>15.378556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.755970</td>\n",
              "      <td>2.280000</td>\n",
              "      <td>20.413403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.844456</td>\n",
              "      <td>15.620000</td>\n",
              "      <td>43.934612</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ed25ef2-955a-428f-9887-32a16de026a1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fb074d47-cbe1-4c0f-b334-dbe81a64c028\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb074d47-cbe1-4c0f-b334-dbe81a64c028')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fb074d47-cbe1-4c0f-b334-dbe81a64c028 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnunet_pz_prostatex_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.10303279619629,\n        \"min\": 0.059166,\n        \"max\": 97.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.673064618556701,\n          0.712014,\n          97.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.39678354526564,\n        \"min\": 0.62,\n        \"max\": 97.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.0061855670103097,\n          1.36,\n          97.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.4332791597264,\n        \"min\": 7.1450754125520115,\n        \"max\": 97.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          16.862657525773194,\n          15.378556,\n          97.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monai_pz_prostatex_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "x1vMDAwt0w27",
        "outputId": "eba95376-b242-46b2-f521-1c74a0d1fdb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  98.000000  98.0  98.000000\n",
              "mean    0.737500   0.0  12.964193\n",
              "std     0.081039   0.0   5.238738\n",
              "min     0.460548   0.0   5.024938\n",
              "25%     0.697544   0.0   9.243224\n",
              "50%     0.756841   0.0  11.788753\n",
              "75%     0.788580   0.0  15.554309\n",
              "max     0.884381   0.0  30.588818"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f76a6831-30fe-478d-818c-801378cb74ca\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.737500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.964193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.081039</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.238738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.460548</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.024938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.697544</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.243224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.756841</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.788753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.788580</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.554309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.884381</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.588818</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f76a6831-30fe-478d-818c-801378cb74ca')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f76a6831-30fe-478d-818c-801378cb74ca button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f76a6831-30fe-478d-818c-801378cb74ca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1adb8368-b302-4221-8b18-1ac2ba75b78c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1adb8368-b302-4221-8b18-1ac2ba75b78c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1adb8368-b302-4221-8b18-1ac2ba75b78c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"monai_pz_prostatex_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.4266119568212,\n        \"min\": 0.08103921649298743,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7374995408163266,\n          0.7568405,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.64823227814083,\n        \"min\": 0.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.144430537158716,\n        \"min\": 5.024938,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.964192908163264,\n          11.788752500000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bt6znmJYM483"
      },
      "source": [
        "#### Transition zone\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M4gjDj4qM483"
      },
      "outputs": [],
      "source": [
        "tz_prostatex_df = data_df[(data_df.collection_id == 'prostatex') & (data_df.SegmentedPropertyTypeCodeMeaning == 'Structure of transition zone of prostate (body structure)')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SrTlsJmGM483"
      },
      "outputs": [],
      "source": [
        "nnunet_tz_prostatex_df =  tz_prostatex_df[tz_prostatex_df.algorithmNameCollection == 'nnunet_prostate_zonal_task05-prostatex']\n",
        "monai_tz_prostatex_df = tz_prostatex_df[tz_prostatex_df.algorithmNameCollection == 'monai_prostate158-prostatex']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(nnunet_tz_prostatex_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6gju6mLJ492",
        "outputId": "1dc764f7-8ba5-4846-942a-6d1d799a1239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(monai_tz_prostatex_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KoNG__duJ89L",
        "outputId": "1d79fff6-8cd7-4314-a215-45cef6b21a94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nnunet_tz_prostatex_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "w0y_76y22fO3",
        "outputId": "9f123a82-1c55-4e25-9092-b079f7977ebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  98.000000  98.0  98.000000\n",
              "mean    0.817036   0.0  16.414104\n",
              "std     0.112423   0.0  14.643398\n",
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              "25%     0.778567   0.0   9.219544\n",
              "50%     0.847678   0.0  11.378697\n",
              "75%     0.893387   0.0  15.376456\n",
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              "      <th>std</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnunet_tz_prostatex_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.41051122774264,\n        \"min\": 0.11242297047916149,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.817036112244898,\n          0.8476779999999999,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.64823227814083,\n        \"min\": 0.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.84239823682613,\n        \"min\": 5.385165,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          16.414103551020407,\n          11.3786965\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monai_tz_prostatex_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 300
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  98.000000  98.0  98.000000\n",
              "mean    0.850958   0.0  11.866882\n",
              "std     0.069328   0.0   2.378171\n",
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              "25%     0.817226   0.0   9.848858\n",
              "50%     0.872059   0.0  11.900212\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-724c511a-a532-4a79-b2bb-bcaff29e44ac button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-724c511a-a532-4a79-b2bb-bcaff29e44ac');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cba7d817-8804-482c-b1b6-b23c5d0a31ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"monai_tz_prostatex_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.394657897202016,\n        \"min\": 0.06932778357166638,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8509582653061224,\n          0.872059,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.64823227814083,\n        \"min\": 0.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.192745017804317,\n        \"min\": 2.378171057199039,\n        \"max\": 98.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.866881857142857,\n          11.900212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oySNimZAM483",
        "outputId": "27b88ccd-765e-4887-8a13-204e45163f44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nnunet task05 model DSC mean and std for tz: 0.82/0.11\n",
            "monai model DSC mean and std for tz: 0.85/0.07\n"
          ]
        }
      ],
      "source": [
        "print(f\"nnunet task05 model DSC mean and std for tz: {round(np.mean(nnunet_tz_prostatex_df.dsc.values),2)}/{round(np.std(nnunet_tz_prostatex_df.dsc.values),2)}\")\n",
        "print(f\"monai model DSC mean and std for tz: {round(np.mean(monai_tz_prostatex_df.dsc.values),2)}/{round(np.std(monai_tz_prostatex_df.dsc.values),2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npBQnfUSM483",
        "outputId": "23237884-0339-4292-ab23-9b9d1568ddd7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(533.0, 1.9973128891640755e-11)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "diff = monai_tz_prostatex_df.sort_values(by=['refSerieUID']).dsc.values - nnunet_tz_prostatex_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwGo6kIHM483",
        "outputId": "ad82f759-5efe-423b-a269-4ee0ed3707e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4318.0, 9.986564445820377e-12)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCktGgNXM484"
      },
      "source": [
        "### QIN collection\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9vtfsFAM484"
      },
      "source": [
        "#### Whole prostate\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sL3GJwhhM484",
        "outputId": "de4a5e13-4f7b-4e0f-fb53-7f02701a7bb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "120"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "prostate_qin_df = data_df[(data_df.collection_id == 'qin_prostate_repeatability') & (data_df.SegmentedPropertyTypeCodeMeaning.isin(['Prostate', 'Prostatic structure']))]\n",
        "len(prostate_qin_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "247Kvmo9M484",
        "outputId": "8c5ca56c-f3e7-4fdf-cfb8-a3d5043b1c46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['bamf_nnunet_mr_prostate-qin_prostate_repeatability',\n",
              "       'nnunet_prostate_zonal_task05-qin_prostate_repeatability',\n",
              "       'monai_prostate158-qin_prostate_repeatability',\n",
              "       'nnunet_prostate_task24-qin_prostate_repeatability'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "prostate_qin_df.algorithmNameCollection.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ss0C13v0M484"
      },
      "outputs": [],
      "source": [
        "nnnunet_task024_prostate_qin_df = prostate_qin_df[prostate_qin_df.algorithmNameCollection == 'nnunet_prostate_task24-qin_prostate_repeatability']\n",
        "nnnunet_task05_prostate_qin_df = prostate_qin_df[prostate_qin_df.algorithmNameCollection == 'nnunet_prostate_zonal_task05-qin_prostate_repeatability']\n",
        "monai_prostate158_prostate_qin_df = prostate_qin_df[prostate_qin_df.algorithmNameCollection == 'monai_prostate158-qin_prostate_repeatability']\n",
        "bamf_nnunet_mr_prostate_qin_df = prostate_qin_df[prostate_qin_df.algorithmNameCollection == 'bamf_nnunet_mr_prostate-qin_prostate_repeatability']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jxz7EFRM484",
        "outputId": "227e9551-e38c-445a-e33c-801887eca087"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nnunet task024 model DSC mean and std for whole prostate: 0.85/0.05\n",
            "nnunet task05 model DSC mean and std for whole prostate: 0.57/0.27\n",
            "nnunet monai prostate158 DSC model mean and std for whole prostate: 0.83/0.05\n",
            "bamf_nnunet_mr_prostate DSC model mean and std for whole prostate: 0.79/0.16\n"
          ]
        }
      ],
      "source": [
        "print(f\"nnunet task024 model DSC mean and std for whole prostate: {round(np.mean(nnnunet_task024_prostate_qin_df.dsc.values),2)}/{round(np.std(nnnunet_task024_prostate_qin_df.dsc.values),2)}\")\n",
        "print(f\"nnunet task05 model DSC mean and std for whole prostate: {round(np.mean(nnnunet_task05_prostate_qin_df.dsc.values),2)}/{round(np.std(nnnunet_task05_prostate_qin_df.dsc.values),2)}\")\n",
        "print(f\"nnunet monai prostate158 DSC model mean and std for whole prostate: {round(np.mean(monai_prostate158_prostate_qin_df.dsc.values),2)}/{round(np.std(monai_prostate158_prostate_qin_df.dsc.values),2)}\")\n",
        "print(f\"bamf_nnunet_mr_prostate DSC model mean and std for whole prostate: {round(np.mean(bamf_nnunet_mr_prostate_qin_df.dsc.values),2)}/{round(np.std(bamf_nnunet_mr_prostate_qin_df.dsc.values),2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfesSu56M484",
        "outputId": "f68fd68a-ca8d-4886-f713-1cc51422377c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nnunet task024 model HSDFF95 mean and std for whole prostate: 2.6/1.9\n",
            "nnunet task05 model HSDFF95 mean and std for whole prostate: 14.82/12.66\n",
            "nnunet monai prostate158 DSC model mean and std for whole prostate: 3.55/2.34\n",
            "bamf_nnunet_mr_prostate DSC model mean and std for whole prostate: 4.73/4.66\n"
          ]
        }
      ],
      "source": [
        "print(f\"nnunet task024 model HSDFF95 mean and std for whole prostate: {round(np.mean(nnnunet_task024_prostate_qin_df.hsdff_95.values),2)}/{round(np.std(nnnunet_task024_prostate_qin_df.hsdff_95.values),2)}\")\n",
        "print(f\"nnunet task05 model HSDFF95 mean and std for whole prostate: {round(np.mean(nnnunet_task05_prostate_qin_df.hsdff_95.values),2)}/{round(np.std(nnnunet_task05_prostate_qin_df.hsdff_95.values),2)}\")\n",
        "print(f\"nnunet monai prostate158 DSC model mean and std for whole prostate: {round(np.mean(monai_prostate158_prostate_qin_df.hsdff_95.values),2)}/{round(np.std(monai_prostate158_prostate_qin_df.hsdff_95.values),2)}\")\n",
        "print(f\"bamf_nnunet_mr_prostate DSC model mean and std for whole prostate: {round(np.mean(bamf_nnunet_mr_prostate_qin_df.hsdff_95.values),2)}/{round(np.std(bamf_nnunet_mr_prostate_qin_df.hsdff_95.values),2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZeZY1tdM484",
        "outputId": "25873b38-d27e-46b6-da72-73a8256b2ad3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49.24000000000001\n",
            "1.1596706843171837e-10\n"
          ]
        }
      ],
      "source": [
        "res = friedmanchisquare(nnnunet_task024_prostate_qin_df.sort_values(by=[\"refSerieUID\"]).dsc.values,\n",
        "                        nnnunet_task05_prostate_qin_df.sort_values(by=[\"refSerieUID\"]).dsc.values,\n",
        "                        monai_prostate158_prostate_qin_df.sort_values(by=[\"refSerieUID\"]).dsc.values,\n",
        "                        bamf_nnunet_mr_prostate_qin_df.sort_values(by=[\"refSerieUID\"]).dsc.values)\n",
        "print(res.statistic)\n",
        "print(res.pvalue)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "31pougkhM484"
      },
      "outputs": [],
      "source": [
        "# sp.posthoc_conover(prostate_qin_df, val_col='dsc', group_col='agorithmNameCollection', p_adjust = 'bonf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qoP32TzGM484"
      },
      "outputs": [],
      "source": [
        "# nnnunet_task024_prostate_qin_df.sort_values(by=['dsc'],\n",
        "#                                                                       ascending=True)[['image_PatientID', 'dsc', 'asd', 'hsdff',\n",
        "#                              'sr_ai_volume_value', 'sr_idc_volume_value', 'ohif_url']].head(8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U_-1WzKnM484"
      },
      "outputs": [],
      "source": [
        "# nnnunet_task05_prostate_qin_df.sort_values(by=['dsc'],\n",
        "#                                                                       ascending=True)[['image_PatientID', 'dsc', 'asd', 'hsdff',\n",
        "#                              'sr_ai_volume_value', 'sr_idc_volume_value', 'ohif_url']].head(8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gvWzHtgQM484"
      },
      "outputs": [],
      "source": [
        "# monai_prostate158_prostate_qin_df.sort_values(by=['dsc'],\n",
        "#                                                                       ascending=True)[['image_PatientID', 'dsc', 'asd', 'hsdff',\n",
        "#                              'sr_ai_volume_value', 'sr_idc_volume_value', 'ohif_url', 'image_studyUID']].head(8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rpXnuUnVM484",
        "outputId": "9b43278c-6383-42e1-cfa0-9b8ea264aab2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  30.000000  30.0  30.000000\n",
              "mean    0.853900   0.0  10.121993\n",
              "std     0.048305   0.0   4.388385\n",
              "min     0.722204   0.0   6.000000\n",
              "25%     0.826551   0.0   7.000376\n",
              "50%     0.861006   0.0   9.120458\n",
              "75%     0.892994   0.0  10.501964\n",
              "max     0.919915   0.0  24.000000"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.853900</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.121993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.048305</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.388385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.722204</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.826551</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.861006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.120458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.892994</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.501964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.919915</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.000000</td>\n",
              "    </tr>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1233a8af-c588-4586-b8be-ec698c32658b');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-19862427-ba27-448d-a20d-db35c37e055d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnnunet_task024_prostate_qin_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.351684847947938,\n        \"min\": 0.04830454576852419,\n        \"max\": 30.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8539004000000001,\n          0.8610055000000001,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.606601717798213,\n        \"min\": 0.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.240454763019223,\n        \"min\": 4.388384720133113,\n        \"max\": 30.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.121993066666667,\n          9.120458\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "nnnunet_task024_prostate_qin_df[['dsc', 'asd', 'hsdff']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "M7JUpp78M484",
        "outputId": "dfd73edb-a122-424b-e20f-8d8d9446276b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  30.000000  30.0  30.000000\n",
              "mean    0.565395   0.0  31.488467\n",
              "std     0.274746   0.0  16.131696\n",
              "min     0.000000   0.0   6.659697\n",
              "25%     0.395273   0.0  20.758088\n",
              "50%     0.607371   0.0  27.254463\n",
              "75%     0.795912   0.0  43.921661\n",
              "max     0.866814   0.0  69.067986"
            ],
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.565395</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.488467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.274746</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.131696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.659697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.395273</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.758088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.607371</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.254463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.795912</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43.921661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.866814</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.067986</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fb799cec-1637-4413-9901-220628b77253');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnnunet_task05_prostate_qin_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.433310139041012,\n        \"min\": 0.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5653954,\n          0.607371,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.606601717798213,\n        \"min\": 0.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.08214828664989,\n        \"min\": 6.659697,\n        \"max\": 69.067986,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.488466733333336,\n          27.254463\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "nnnunet_task05_prostate_qin_df[['dsc', 'asd', 'hsdff']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "c3oEat0CM484",
        "outputId": "a6154263-0ad3-4928-8941-ac8d80f479a3"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  30.000000  30.0  30.000000\n",
              "mean    0.830429   0.0  13.562133\n",
              "std     0.051353   0.0   7.740125\n",
              "min     0.703789   0.0   5.635060\n",
              "25%     0.796378   0.0   7.972199\n",
              "50%     0.842634   0.0  10.729490\n",
              "75%     0.874580   0.0  16.918558\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.830429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.562133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.051353</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.740125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0.703789</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.635060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.796378</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.972199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.842634</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.729490</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>16.918558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.897631</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36.377495</td>\n",
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              "\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    padding: 0;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef265b27-4de7-4425-be50-7d34db2336e5 button');\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"monai_prostate158_prostate_qin_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.35791969988588,\n        \"min\": 0.05135312825288709,\n        \"max\": 30.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8304287666666668,\n          0.842634,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.606601717798213,\n        \"min\": 0.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.249135440978522,\n        \"min\": 5.63506,\n        \"max\": 36.377495,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13.562132833333331,\n          10.72949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "monai_prostate158_prostate_qin_df[['dsc', 'asd', 'hsdff']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bamf_nnunet_mr_prostate_qin_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "coMzsZeoMHn_",
        "outputId": "c6bfbac5-0f6f-46c4-d56a-bb14fa8f06b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  30.000000  30.0  30.000000\n",
              "mean    0.791851   0.0  12.053428\n",
              "std     0.166551   0.0   9.475963\n",
              "min     0.192064   0.0   3.998422\n",
              "25%     0.797459   0.0   5.376774\n",
              "50%     0.845625   0.0   7.349561\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.791851</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.053428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.166551</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.475963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.192064</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.998422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.797459</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.376774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.845625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.349561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.891563</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.815395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.917200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2deda4f0-7356-48ca-9c72-b30f41629c0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"bamf_nnunet_mr_prostate_qin_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.378644375517396,\n        \"min\": 0.16655102798099922,\n        \"max\": 30.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7918512999999999,\n          0.8456245,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.606601717798213,\n        \"min\": 0.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.48038832849816,\n        \"min\": 3.998422,\n        \"max\": 38.5,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.0534278,\n          7.349561\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uH1OexGPM484"
      },
      "source": [
        "Pair 1 : task024 dsc differences vs task05\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPrspW6eM484",
        "outputId": "b6e88b10-e8fe-4ad5-c3df-c5dfd668e753"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 3.725290298461914e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ],
      "source": [
        "diff = nnnunet_task024_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dO6wcmHM484",
        "outputId": "47504fc5-f15b-45d2-fb8f-7ae6725dcff7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(464.0, 1.862645149230957e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVS-q7iPM485"
      },
      "source": [
        "Pair 2 : task024 dsc differences vs monai\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzq3q28dM485",
        "outputId": "b0c082be-f065-4bea-880a-607729b57ac9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72.0, 0.0005548261106014252)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "diff = nnnunet_task024_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - monai_prostate158_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9D3GIr_M485",
        "outputId": "a7f8be6b-c383-4568-c79f-cd8e2a9204fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(393.0, 0.0002774130553007126)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GHVo7sBM485"
      },
      "source": [
        "Pair 3 : task05 dsc differences vs monai\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjN76x5ZM485",
        "outputId": "a63dd3cb-ea31-4c35-cd8d-9b464d2ccd37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26.0, 1.991167664527893e-06)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ],
      "source": [
        "diff = monai_prostate158_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1K8fCtbEM485",
        "outputId": "d8b1c86f-55b0-461a-c415-9e50eeab558c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(439.0, 0.9999991580843925)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='less')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 4 : bamf_nnunet_mr_prostate vs nnunet_task024"
      ],
      "metadata": {
        "id": "jAqYP4XgMWnX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a439d26-fb55-4213-bc3f-5050777770a5",
        "id": "EZipULGQMWnY"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(230.0, 0.9676735773682594)"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task024_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5978f343-c99e-494b-f511-1c2ec90c0335",
        "id": "c4K-lz_dMWnY"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(230.0, 0.5242367628961802)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 5 : bamf_nnunet_mr_prostate vs nnunet_task05"
      ],
      "metadata": {
        "id": "F0Xq6XX3MWnZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60b971c1-839a-4dc0-9468-723e4fb15a95",
        "id": "Q7VWU47_MWnZ"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15.0, 2.551823854446411e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c907f5c-6222-4cb8-d134-94f4f68e9019",
        "id": "ILMme3bWMWnZ"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(450.0, 1.2759119272232056e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 6 : bamf_nnunet_mr_prostate vs monai prostate158"
      ],
      "metadata": {
        "id": "B9QM4yrhMWnZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b61c464c-9e9e-4f28-91db-aa00e72b6ae6",
        "id": "ZP_sW6dbMWnZ"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(203.0, 0.5561132747679949)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - monai_prostate158_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25cb8c57-aefd-42e2-cad6-09627d3da3b8",
        "id": "aUL3qQTrMWnZ"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(262.0, 0.27805663738399744)"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTsva6BrM485"
      },
      "source": [
        "#### Peripheral zone\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.SegmentedPropertyTypeCodeMeaning.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ss71fMKjM7eX",
        "outputId": "644ccefa-5f7c-4c3c-c7ae-d99e20776058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Prostate', 'Prostatic structure',\n",
              "       'Structure of peripheral glandular zone of prostate (body structure)',\n",
              "       'Structure of transition zone of prostate (body structure)'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S_2ICc_QM485"
      },
      "outputs": [],
      "source": [
        "pz_qin_df = data_df[(data_df.collection_id == 'qin_prostate_repeatability') & (data_df.SegmentedPropertyTypeCodeMeaning == \"Structure of peripheral glandular zone of prostate (body structure)\")]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTCc9u07M485",
        "outputId": "4383e5de-8c51-488f-dec7-5b62e8a67985"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['nnunet_prostate_zonal_task05-qin_prostate_repeatability',\n",
              "       'monai_prostate158-qin_prostate_repeatability'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "pz_qin_df.algorithmNameCollection.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0_99lT78M485"
      },
      "outputs": [],
      "source": [
        "monai_prostate158_pz_qin_df = pz_qin_df[pz_qin_df.algorithmNameCollection == 'monai_prostate158-qin_prostate_repeatability']\n",
        "nnnunet_task05_pz_qin_df = pz_qin_df[pz_qin_df.algorithmNameCollection == 'nnunet_prostate_zonal_task05-qin_prostate_repeatability']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(monai_prostate158_pz_qin_df))\n",
        "print(len(nnnunet_task05_pz_qin_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suXk7na5NX1N",
        "outputId": "51fbaf4a-003f-4685-e6a2-d5fd36cb7865"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30\n",
            "29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7cbQ7DibM485"
      },
      "outputs": [],
      "source": [
        "# nnnunet_task05_pz_qin_df = nnnunet_task05_pz_qin_df.drop_duplicates(subset='refSerieUID', keep=\"last\")\n",
        "# monai_prostate158_pz_qin_df = monai_prostate158_pz_qin_df.drop_duplicates(subset='refSerieUID', keep=\"last\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jducbHFM485",
        "outputId": "0a6e2874-c057-4856-b855-9ccc3f364097"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nnunet task05 model DSC mean and std for pz: 0.39/0.3\n",
            "nnunet monai prostate158 DSC model mean and std for pz: 0.65/0.16\n"
          ]
        }
      ],
      "source": [
        "print(f\"nnunet task05 model DSC mean and std for pz: {round(np.mean(nnnunet_task05_pz_qin_df.dsc.values),2)}/{round(np.std(nnnunet_task05_pz_qin_df.dsc.values),2)}\")\n",
        "print(f\"nnunet monai prostate158 DSC model mean and std for pz: {round(np.mean(monai_prostate158_pz_qin_df.dsc.values),2)}/{round(np.std(monai_prostate158_pz_qin_df.dsc.values),2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYMJiGlFM485",
        "outputId": "47f7a7aa-edf0-442d-e386-86686e607821"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19.0, 1.1436641216278076e-06)"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ],
      "source": [
        "diff = monai_prostate158_pz_qin_df[monai_prostate158_pz_qin_df.refSerieUID.isin(nnnunet_task05_pz_qin_df.refSerieUID.unique())].sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_pz_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7Aiyrk-M485",
        "outputId": "6d46574a-df49-42b8-f269-06c2462040a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(416.0, 5.718320608139038e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nnnunet_task05_pz_qin_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "uIWGyLZ_eYdk",
        "outputId": "eaa8d24d-4b76-457b-aade-70d57a8a3014"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             dsc        asd       hsdff\n",
              "count  29.000000  29.000000   29.000000\n",
              "mean    0.385754   1.594828   28.658644\n",
              "std     0.301435   3.883204   27.453498\n",
              "min     0.000000   0.000000    5.304774\n",
              "25%     0.008434   0.000000    9.919932\n",
              "50%     0.459504   0.000000   15.856929\n",
              "75%     0.641352   1.090000   34.814823\n",
              "max     0.797521  17.100000  119.323219"
            ],
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              "      <td>1.594828</td>\n",
              "      <td>28.658644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.301435</td>\n",
              "      <td>3.883204</td>\n",
              "      <td>27.453498</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monai_prostate158_pz_qin_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
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        "id": "jOl7mR2xeY0t",
        "outputId": "f86d3e31-058f-47f5-ed6e-12bef0f9163e"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "             dsc   asd      hsdff\n",
              "count  30.000000  30.0  30.000000\n",
              "mean    0.646908   0.0  14.345313\n",
              "std     0.162654   0.0   8.732322\n",
              "min     0.145839   0.0   6.144385\n",
              "25%     0.623231   0.0   9.299276\n",
              "50%     0.708787   0.0  12.778872\n",
              "75%     0.746650   0.0  15.439749\n",
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              "      <td>30.000000</td>\n",
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              "      <th>mean</th>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"monai_prostate158_pz_qin_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.416695442162336,\n        \"min\": 0.145839,\n        \"max\": 30.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6469084333333334,\n          0.7087870000000001,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.606601717798213,\n        \"min\": 0.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.562582952241035,\n        \"min\": 6.144385,\n        \"max\": 46.185635,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.345312933333336,\n          12.778871500000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ujRhb7EM485"
      },
      "source": [
        "### Prostate MRI US Biopsy\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVDD_VP1M486"
      },
      "source": [
        "#### Whole prostate\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PuO2cdPSM486",
        "outputId": "979ac96f-6a9f-4e60-86e5-45d5a1d41d87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 3947 entries, 76 to 4908\n",
            "Data columns (total 62 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   segmentationInstanceUID               3945 non-null   object \n",
            " 1   sourceSegmentedSeriesUID              3945 non-null   object \n",
            " 2   expert_QuantityCodeMeaning            3947 non-null   object \n",
            " 3   expert_QuantityCodeValue              3947 non-null   object \n",
            " 4   expert_Value                          3947 non-null   object \n",
            " 5   expert_UnitsCodeValue                 3947 non-null   object \n",
            " 6   expert_UnitsCodeMeaning               3947 non-null   object \n",
            " 7   expert_findingSiteCodeValue           3947 non-null   object \n",
            " 8   expert_findingSiteCodeMeaning         3947 non-null   object \n",
            " 9   ai_QuantityCodeMeaning                3945 non-null   object \n",
            " 10  ai_QuantityCodeValue                  3945 non-null   object \n",
            " 11  ai_QuantityCodingSchemeDesignator     3945 non-null   object \n",
            " 12  ai_Value                              3945 non-null   object \n",
            " 13  ai_UnitsCodeValue                     3945 non-null   object \n",
            " 14  ai_UnitsCodeMeaning                   3945 non-null   object \n",
            " 15  ai_findingSiteCodeValue               3945 non-null   object \n",
            " 16  ai_findingSiteCodeMeaning             3945 non-null   object \n",
            " 17  StudyInstanceUID                      3947 non-null   object \n",
            " 18  SeriesInstanceUID                     3947 non-null   object \n",
            " 19  SegmentedPropertyCategoryCodeValue    3947 non-null   object \n",
            " 20  SegmentedPropertyCategoryCodeMeaning  3947 non-null   object \n",
            " 21  SegmentedPropertyTypeCodeValue        3947 non-null   object \n",
            " 22  SegmentedPropertyTypeCodeMeaning      3947 non-null   object \n",
            " 23  SegmentAlgorithmType                  3947 non-null   object \n",
            " 24  SegmentNumber                         3947 non-null   Int64  \n",
            " 25  segmented_SeriesInstanceUID           3947 non-null   object \n",
            " 26  expert_measurements_SOPInstanceUID    3947 non-null   object \n",
            " 27  segmentation_SOPInstanceUID           3947 non-null   object \n",
            " 28  refSerieUID                           3947 non-null   object \n",
            " 29  ai_seg_serieUID                       3947 non-null   object \n",
            " 30  idc_seg_serieUID                      3947 non-null   object \n",
            " 31  aiSegmentNumber                       3947 non-null   Int64  \n",
            " 32  idcSegmentNumber                      3947 non-null   Int64  \n",
            " 33  idcSegmentCodeMeaning                 3947 non-null   object \n",
            " 34  aiSegmentCodeMeaning                  3947 non-null   object \n",
            " 35  aiSegmentCodeValue                    3947 non-null   Int64  \n",
            " 36  idcSegmentCodeValue                   3947 non-null   object \n",
            " 37  aiSegmentCodingScheme                 3947 non-null   object \n",
            " 38  idcSegmentCodingScheme                3947 non-null   object \n",
            " 39  dsc                                   3947 non-null   float64\n",
            " 40  hsdff                                 3947 non-null   float64\n",
            " 41  hsdff_95                              3947 non-null   float64\n",
            " 42  tp                                    3947 non-null   Int64  \n",
            " 43  fp                                    3947 non-null   Int64  \n",
            " 44  tn                                    3947 non-null   Int64  \n",
            " 45  fn                                    3947 non-null   Int64  \n",
            " 46  asd                                   3947 non-null   float64\n",
            " 47  collection_id                         3947 non-null   object \n",
            " 48  image_SeriesInstanceUID               3947 non-null   object \n",
            " 49  image_PixelSpacing                    3947 non-null   object \n",
            " 50  image_SliceThickness                  3947 non-null   float64\n",
            " 51  image_MagneticFieldStrength           3947 non-null   object \n",
            " 52  image_SeriesDate                      3947 non-null   dbdate \n",
            " 53  image_StudyDate                       3947 non-null   dbdate \n",
            " 54  image_StudyDescription                3947 non-null   object \n",
            " 55  image_StudyDescription_1              3947 non-null   object \n",
            " 56  image_Manufacturer                    3947 non-null   object \n",
            " 57  image_ManufacturerModelName           3947 non-null   object \n",
            " 58  image_PatientID                       3947 non-null   object \n",
            " 59  SegmentAlgorithmName                  3947 non-null   object \n",
            " 60  ohif_url                              3947 non-null   object \n",
            " 61  algorithmNameCollection               3947 non-null   object \n",
            "dtypes: Int64(8), dbdate(2), float64(5), object(47)\n",
            "memory usage: 1.9+ MB\n"
          ]
        }
      ],
      "source": [
        "prostate_mri_us_biopsy_df = data_df[(data_df.collection_id == 'prostate_mri_us_biopsy') & (data_df.SegmentedPropertyTypeCodeMeaning.isin(['Prostate', 'Prostatic structure']))]\n",
        "prostate_mri_us_biopsy_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnkvffHfM486",
        "outputId": "7fc11141-494c-4542-fad5-c16bd5a9e53d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['bamf_nnunet_mr_prostate-prostate_mri_us_biopsy',\n",
              "       'nnunet_prostate_task24-prostate_mri_us_biopsy',\n",
              "       'nnunet_prostate_zonal_task05-prostate_mri_us_biopsy',\n",
              "       'monai_prostate158-prostate_mri_us_biopsy'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ],
      "source": [
        "prostate_mri_us_biopsy_df.algorithmNameCollection.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OBW-KQkXM486"
      },
      "outputs": [],
      "source": [
        "nnunet_task024_prostate_prostate_mri_us_biopsy_df = prostate_mri_us_biopsy_df[prostate_mri_us_biopsy_df.algorithmNameCollection \\\n",
        "    == 'nnunet_prostate_task24-prostate_mri_us_biopsy']\n",
        "monai_prostate158_prostate_prostate_mri_us_biopsy_df = prostate_mri_us_biopsy_df[prostate_mri_us_biopsy_df.algorithmNameCollection \\\n",
        "    == 'monai_prostate158-prostate_mri_us_biopsy']\n",
        "nnunet_task05_prostate_mri_us_biopsy_df = prostate_mri_us_biopsy_df[prostate_mri_us_biopsy_df.algorithmNameCollection \\\n",
        "    == 'nnunet_prostate_zonal_task05-prostate_mri_us_biopsy']\n",
        "bamf_nnunet_mr_prostate_prostate_mri_us_biopsy_df = prostate_mri_us_biopsy_df[prostate_mri_us_biopsy_df.algorithmNameCollection \\\n",
        "    == 'bamf_nnunet_mr_prostate-prostate_mri_us_biopsy']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_ZPv9mBM486",
        "outputId": "85583704-2a32-4d17-c82f-a929c48021a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nnunet task024 model DSC mean and std for prostate:       0.86/0.09\n",
            "nnunet monai prostate158 DSC model mean and std for prostate:       0.88/0.07\n",
            "nnunet_prostate_zonal_task05 DSC model mean and std for prostate:       0.79/0.16\n",
            "bamf_nnunet_mr_prostate DSC model mean and std for prostate:       0.89/0.05\n"
          ]
        }
      ],
      "source": [
        "print(f\"nnunet task024 model DSC mean and std for prostate: \\\n",
        "      {round(np.mean(nnunet_task024_prostate_prostate_mri_us_biopsy_df.dsc.values),2)}/{round(np.std(nnunet_task024_prostate_prostate_mri_us_biopsy_df.dsc.values),2)}\")\n",
        "print(f\"nnunet monai prostate158 DSC model mean and std for prostate: \\\n",
        "      {round(np.mean(monai_prostate158_prostate_prostate_mri_us_biopsy_df.dsc.values),2)}/{round(np.std(monai_prostate158_prostate_prostate_mri_us_biopsy_df.dsc.values),2)}\")\n",
        "print(f\"nnunet_prostate_zonal_task05 DSC model mean and std for prostate: \\\n",
        "      {round(np.mean(nnunet_task05_prostate_mri_us_biopsy_df.dsc.values),2)}/{round(np.std(nnunet_task05_prostate_mri_us_biopsy_df.dsc.values),2)}\")\n",
        "print(f\"bamf_nnunet_mr_prostate DSC model mean and std for prostate: \\\n",
        "      {round(np.mean(bamf_nnunet_mr_prostate_prostate_mri_us_biopsy_df.dsc.values),2)}/{round(np.std(bamf_nnunet_mr_prostate_prostate_mri_us_biopsy_df.dsc.values),2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(nnunet_task024_prostate_prostate_mri_us_biopsy_df))\n",
        "print(len(monai_prostate158_prostate_prostate_mri_us_biopsy_df))\n",
        "print(len(nnunet_task05_prostate_mri_us_biopsy_df))\n",
        "print(len(bamf_nnunet_mr_prostate_prostate_mri_us_biopsy_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CxKRXaePsTf",
        "outputId": "26625ff2-cd9a-449c-eddc-bc90cd0663a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "987\n",
            "987\n",
            "987\n",
            "986\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert sorted(nnunet_task024_prostate_prostate_mri_us_biopsy_df.refSerieUID.unique()) == sorted(monai_prostate158_prostate_prostate_mri_us_biopsy_df.refSerieUID.unique()) \\\n",
        "== sorted(nnunet_task05_prostate_mri_us_biopsy_df.refSerieUID.unique()) \\\n",
        "# == sorted(bamf_nnunet_mr_prostate_prostate_mri_us_biopsy_df.refSerieUID.unique())"
      ],
      "metadata": {
        "id": "9272FyaqQED4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6j_xQiFlM486",
        "outputId": "470dda94-e557-4859-bdaf-b0f35f729373"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Statistics=0.621, p=0.000\n",
            "Sample does not look Gaussian (reject H0)\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "stat, p = shapiro(nnunet_task024_prostate_prostate_mri_us_biopsy_df.dsc.values)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p > alpha:\n",
        "    print('Sample looks Gaussian (fail to reject H0)')\n",
        "else:\n",
        "    print('Sample does not look Gaussian (reject H0)')\n",
        "print(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "znxJhxsCM486"
      },
      "outputs": [],
      "source": [
        "# from statsmodels.stats.anova import AnovaRM\n",
        "\n",
        "# # Create the data\n",
        "\n",
        "# # Conduct the repeated measures ANOVA\n",
        "# print(AnovaRM(data=prostate_mri_us_biopsy_df, depvar='dsc',\n",
        "#               subject='refSerieUID', within=['algorithmNameCollection']).fit())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mHpfWsYM487"
      },
      "source": [
        "A one-way repeated measures ANOVA is conducted on 1017 individuals to interpret the effect of 2 different models on the dice score relative to the ground truth for prostate mri us biopsy collection. Results showed that the the two models are statistically significant different in response time (F(2, 1016) = 12.91, p < 0.0003).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5nZZCVquM487",
        "outputId": "67f04626-71bd-4562-e4ed-e3278c577eed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              dsc         asd       hsdff\n",
              "count  987.000000  987.000000  987.000000\n",
              "mean     0.860779    2.322057   12.123365\n",
              "std      0.088090    5.260633   16.960390\n",
              "min      0.098355    0.470000    3.327098\n",
              "25%      0.847275    1.070000    6.141153\n",
              "50%      0.883263    1.340000    7.616687\n",
              "75%      0.907840    1.815000   10.139081\n",
              "max      0.945701   55.300000  123.988251"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32627b81-7eac-4629-ba85-4cb7abfa4de2\" class=\"colab-df-container\">\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>987.000000</td>\n",
              "      <td>987.000000</td>\n",
              "      <td>987.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.860779</td>\n",
              "      <td>2.322057</td>\n",
              "      <td>12.123365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.088090</td>\n",
              "      <td>5.260633</td>\n",
              "      <td>16.960390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.098355</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>3.327098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.847275</td>\n",
              "      <td>1.070000</td>\n",
              "      <td>6.141153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.883263</td>\n",
              "      <td>1.340000</td>\n",
              "      <td>7.616687</td>\n",
              "    </tr>\n",
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              "      <th>75%</th>\n",
              "      <td>0.907840</td>\n",
              "      <td>1.815000</td>\n",
              "      <td>10.139081</td>\n",
              "    </tr>\n",
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              "      <td>0.945701</td>\n",
              "      <td>55.300000</td>\n",
              "      <td>123.988251</td>\n",
              "    </tr>\n",
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              "\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "        document.querySelector('#df-36ca83e6-1ee7-472b-88fe-d0e01a990a2a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnunet_task024_prostate_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348.7234669673271,\n        \"min\": 0.0880896583802858,\n        \"max\": 987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8607789138804458,\n          0.883263,\n          987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 346.0490982540089,\n        \"min\": 0.47,\n        \"max\": 987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.3220567375886527,\n          1.34,\n          987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 342.23296462147556,\n        \"min\": 3.327098,\n        \"max\": 987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.123364580547111,\n          7.616687,\n          987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 157
        }
      ],
      "source": [
        "nnunet_task024_prostate_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monai_prostate158_prostate_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "OkmkfK7Vzjzg",
        "outputId": "920f8b5d-24d7-4f6a-f3fa-629072c846d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              dsc    asd       hsdff\n",
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              "75%      0.911101    0.0    9.000000\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"monai_prostate158_prostate_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348.7210113875452,\n        \"min\": 0.07045734328593922,\n        \"max\": 987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.877274323201621,\n          0.890998,\n          987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348.9571965155612,\n        \"min\": 0.0,\n        \"max\": 987.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344.2248375026341,\n        \"min\": 3.280841,\n        \"max\": 987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.167895931104356,\n          7.386908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nnunet_task05_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "e6lZ4oWwRFg6",
        "outputId": "fed702b5-70af-4dd0-8d8c-544598dd0bcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              dsc    asd       hsdff\n",
              "count  987.000000  987.0  987.000000\n",
              "mean     0.791721    0.0   34.605413\n",
              "std      0.159527    0.0   34.116988\n",
              "min      0.000000    0.0    3.705794\n",
              "25%      0.765055    0.0    7.920339\n",
              "50%      0.849600    0.0   13.516323\n",
              "75%      0.885800    0.0   70.174370\n",
              "max      0.935007    0.0  140.562119"
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              "    </tr>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a3cdf96-bcb0-4da0-8bc1-0e01c2a5a647 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nnunet_task05_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348.73581228339435,\n        \"min\": 0.0,\n        \"max\": 987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7917205450861196,\n          0.8496,\n          987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348.9571965155612,\n        \"min\": 0.0,\n        \"max\": 987.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.5672414043588,\n        \"min\": 3.705794,\n        \"max\": 987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.605413257345496,\n          13.516323\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bamf_nnunet_mr_prostate_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']].describe()"
      ],
      "metadata": {
        "id": "BqNerwbNRtkw",
        "outputId": "7932cb93-be0e-4e0e-9dba-3846dbbf84a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              dsc         asd       hsdff\n",
              "count  986.000000  986.000000  986.000000\n",
              "mean     0.888093    1.274655    7.354920\n",
              "std      0.053950    0.728535    4.994203\n",
              "min      0.127535    0.260000    2.394311\n",
              "25%      0.871351    0.910000    5.269408\n",
              "50%      0.898211    1.130000    6.450957\n",
              "75%      0.919391    1.447500    7.996040\n",
              "max      0.955951   12.390000   77.254585"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3b03d9c-a429-4018-bbc6-0b29fd52adeb\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dsc</th>\n",
              "      <th>asd</th>\n",
              "      <th>hsdff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>986.000000</td>\n",
              "      <td>986.000000</td>\n",
              "      <td>986.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.888093</td>\n",
              "      <td>1.274655</td>\n",
              "      <td>7.354920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.053950</td>\n",
              "      <td>0.728535</td>\n",
              "      <td>4.994203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.127535</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.394311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.871351</td>\n",
              "      <td>0.910000</td>\n",
              "      <td>5.269408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.898211</td>\n",
              "      <td>1.130000</td>\n",
              "      <td>6.450957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.919391</td>\n",
              "      <td>1.447500</td>\n",
              "      <td>7.996040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.955951</td>\n",
              "      <td>12.390000</td>\n",
              "      <td>77.254585</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "        document.querySelector('#df-b3b03d9c-a429-4018-bbc6-0b29fd52adeb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b3b03d9c-a429-4018-bbc6-0b29fd52adeb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4968a3e4-e8b5-408f-930c-abb95fc674a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"bamf_nnunet_mr_prostate_prostate_mri_us_biopsy_df[['dsc', 'asd', 'hsdff']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"dsc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348.3657222868144,\n        \"min\": 0.05395009530832227,\n        \"max\": 986.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8880926277890467,\n          0.8982105,\n          986.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 347.7105979864658,\n        \"min\": 0.26,\n        \"max\": 986.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.2746551724137931,\n          1.13,\n          986.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsdff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 343.8771040832022,\n        \"min\": 2.394311,\n        \"max\": 986.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.354920249492901,\n          6.450957,\n          986.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeFe_1fOQr-Y"
      },
      "source": [
        "Pair 1 : task024 dsc differences vs task05\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6e88b10-e8fe-4ad5-c3df-c5dfd668e753",
        "id": "id7KigNJQr-j"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 3.725290298461914e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ],
      "source": [
        "diff = nnnunet_task024_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47504fc5-f15b-45d2-fb8f-7ae6725dcff7",
        "id": "OuyvgT9zQr-j"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(464.0, 1.862645149230957e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbzgIQwyQr-k"
      },
      "source": [
        "Pair 2 : task024 dsc differences vs monai\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0c082be-f065-4bea-880a-607729b57ac9",
        "id": "BSjTiW9RQr-k"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72.0, 0.0005548261106014252)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "diff = nnnunet_task024_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - monai_prostate158_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7f8be6b-c383-4568-c79f-cd8e2a9204fc",
        "id": "0AdhKH22Qr-k"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(393.0, 0.0002774130553007126)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SIdQjUZpQr-k"
      },
      "source": [
        "Pair 3 : task05 dsc differences vs monai\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a63dd3cb-ea31-4c35-cd8d-9b464d2ccd37",
        "id": "lHlxjlyYQr-k"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26.0, 1.991167664527893e-06)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ],
      "source": [
        "diff = monai_prostate158_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8b1c86f-55b0-461a-c415-9e50eeab558c",
        "id": "OMYDKkgEQr-k"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(439.0, 0.9999991580843925)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='less')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 4 : bamf_nnunet_mr_prostate vs nnunet_task024"
      ],
      "metadata": {
        "id": "qS0-3CvUQr-k"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a439d26-fb55-4213-bc3f-5050777770a5",
        "id": "TNg_C3aRQr-k"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(230.0, 0.9676735773682594)"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task024_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5978f343-c99e-494b-f511-1c2ec90c0335",
        "id": "dDZ24hi-Qr-k"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(230.0, 0.5242367628961802)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 5 : bamf_nnunet_mr_prostate vs nnunet_task05"
      ],
      "metadata": {
        "id": "VFn_bBDYQr-l"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60b971c1-839a-4dc0-9468-723e4fb15a95",
        "id": "7Sq8GOs1Qr-l"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15.0, 2.551823854446411e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - nnnunet_task05_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c907f5c-6222-4cb8-d134-94f4f68e9019",
        "id": "NMJpw2n9Qr-l"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(450.0, 1.2759119272232056e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair 6 : bamf_nnunet_mr_prostate vs monai prostate158"
      ],
      "metadata": {
        "id": "lLzm0R9cQr-l"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b61c464c-9e9e-4f28-91db-aa00e72b6ae6",
        "id": "ub4cRgiLQr-l"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(203.0, 0.5561132747679949)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "diff = bamf_nnunet_mr_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values - monai_prostate158_prostate_qin_df.sort_values(by=['refSerieUID']).dsc.values\n",
        "res = wilcoxon(diff)\n",
        "res.statistic, res.pvalue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25cb8c57-aefd-42e2-cad6-09627d3da3b8",
        "id": "EOC4fPJKQr-l"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(262.0, 0.27805663738399744)"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "res = wilcoxon(diff, alternative='greater')\n",
        "res.statistic, res.pvalue"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.15"
    },
    "colab": {
      "provenance": []
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  "nbformat_minor": 0
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