analysis_notebooks/stats_summary.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Functions"
      ],
      "metadata": {
        "id": "dEAIjbfQY71X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_missing_PID_df(orig_df, model_df):\n",
        "  unique_PID_cases = orig_df.PID.unique()\n",
        "  out_df = model_df.copy()\n",
        "  for PID_current in unique_PID_cases:\n",
        "    if PID_current not in model_df.PID.unique():\n",
        "      #retrieve metadata from orig_df\n",
        "      orig_caseID_serie = orig_df[orig_df[\"PID\"] == PID_current].head(1).squeeze()\n",
        "      #overwrite values with volume=0mL\n",
        "      orig_caseID_serie[\"ai_Value\"] = 0\n",
        "      orig_caseID_serie[\"SegmentAlgorithmName\"] = model_df.SegmentAlgorithmName.unique()[0]\n",
        "      #add to current model_df\n",
        "      out_df = pd.concat([out_df, pd.DataFrame([orig_caseID_serie])],\n",
        "                           ignore_index=True)\n",
        "  return out_df"
      ],
      "metadata": {
        "id": "CxZNoQUUrQKW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "-Kmci8NbL28K"
      },
      "outputs": [],
      "source": [
        "def calculate_time_index(row, df_input, studyDateColName=\"image_StudyDate\"): #assumes only two studies per PatientID\n",
        "  other_study_time = df_input[(df_input[\"image_PatientID\"] == row[\"image_PatientID\"])\n",
        "    & (df_input[\"StudyInstanceUID\"] != row[\"StudyInstanceUID\"])][studyDateColName].values[0]\n",
        "  if row[studyDateColName] < other_study_time:\n",
        "    return \"T0\"\n",
        "  elif row[studyDateColName] > other_study_time:\n",
        "    return \"T1\"\n",
        "  else:\n",
        "    return \"T\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_time_index_prostatex_inf_only(row, df_input,\n",
        "                         seriesTimecolName=\"image_SeriesTime\"): #assumes only two studies per PatientID\n",
        "  seriesTimesLst = df_input[(df_input[\"image_PatientID\"] == row[\"image_PatientID\"])\n",
        "    & (df_input[\"StudyInstanceUID\"] == row[\"StudyInstanceUID\"])][seriesTimecolName].unique()\n",
        "  for idx, el in enumerate(sorted(seriesTimesLst)):\n",
        "    if el == row[seriesTimecolName]:\n",
        "      return f\"T{idx}\""
      ],
      "metadata": {
        "id": "JdbW7aM2U207"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "OPpP_FTUY9rd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "QmBHA0-PL28L"
      },
      "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",
        "from statsmodels.stats.multitest import multipletests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "wgzH3GwCL28M"
      },
      "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"
      ]
    },
    {
      "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": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data import"
      ],
      "metadata": {
        "id": "MI2K6ymUY_Ay"
      }
    },
    {
      "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_new_df = selection_result.result().to_dataframe()\n",
        "# data_new_df[\"algorithmNameCollection\"] = data_new_df.apply(lambda x : x[\"SegmentAlgorithmName\"]+\"-\"+x[\"collection_id\"], axis=1)\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_new_df = pd.read_csv(\"analysis_results.csv\")\n",
        "\n",
        "data_new_df[\"ai_Value\"] = data_new_df.apply(lambda x : x[\"ai_Value\"]/1000 if x[\"ai_Value\"] is not None else None, axis=1)\n",
        "data_new_df[\"expert_Value\"] = data_new_df.apply(lambda x : x[\"expert_Value\"]/1000 if x[\"expert_Value\"] is not None else None, axis=1)"
      ],
      "metadata": {
        "id": "Svooa2lmre2C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7f547fd-b413-417c-f211-8073c6dbb30b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-11-08 15:38:21--  https://raw.githubusercontent.com/ccosmin97/idc-prostate-mri-analysis/refs/heads/main/analysis_results.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7256924 (6.9M) [text/plain]\n",
            "Saving to: ‘analysis_results.csv’\n",
            "\n",
            "\ranalysis_results.cs   0%[                    ]       0  --.-KB/s               \ranalysis_results.cs 100%[===================>]   6.92M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2024-11-08 15:38:22 (73.3 MB/s) - ‘analysis_results.csv’ saved [7256924/7256924]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_new_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhcYK_luZ4Fd",
        "outputId": "331c4c67-f9f5-49f4-8829-a464b2c2880b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4909 entries, 0 to 4908\n",
            "Data columns (total 61 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   float64\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   float64\n",
            " 13  ai_UnitsCodeValue                     4907 non-null   object \n",
            " 14  ai_UnitsCodeMeaning                   4907 non-null   object \n",
            " 15  ai_findingSiteCodeValue               4907 non-null   float64\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   int64  \n",
            " 20  SegmentedPropertyCategoryCodeMeaning  4909 non-null   object \n",
            " 21  SegmentedPropertyTypeCodeValue        4909 non-null   int64  \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  collection_id                         4909 non-null   object \n",
            " 47  image_SeriesInstanceUID               4909 non-null   object \n",
            " 48  image_PixelSpacing                    4909 non-null   object \n",
            " 49  image_SliceThickness                  4909 non-null   float64\n",
            " 50  image_MagneticFieldStrength           4909 non-null   float64\n",
            " 51  image_SeriesDate                      4909 non-null   object \n",
            " 52  image_StudyDate                       4909 non-null   object \n",
            " 53  image_StudyDescription                4909 non-null   object \n",
            " 54  image_StudyDescription_1              4909 non-null   object \n",
            " 55  image_Manufacturer                    4909 non-null   object \n",
            " 56  image_ManufacturerModelName           4909 non-null   object \n",
            " 57  image_PatientID                       4909 non-null   object \n",
            " 58  SegmentAlgorithmName                  4909 non-null   object \n",
            " 59  ohif_url                              4909 non-null   object \n",
            " 60  algorithmNameCollection               4909 non-null   object \n",
            "dtypes: float64(8), int64(10), object(43)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation Statistics"
      ],
      "metadata": {
        "id": "LcM2BrtTvQKH"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZQ1UWdoL28N"
      },
      "source": [
        "### Whole prostate quantitative analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_new_df.collection_id.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-DYoMrAO3Rk2",
        "outputId": "7abc1c88-7428-4819-d8c0-5f5c506a390b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['prostatex', 'prostate_mri_us_biopsy',\n",
              "       'qin_prostate_repeatability'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_new_df.idcSegmentCodeMeaning.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fuvd3vfE0IVE",
        "outputId": "bcb10389-dccd-40eb-e87a-e8d37b599aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Prostate', 'Peripheral_zone_of_prostate',\n",
              "       'Transition_zone_of_prostate', 'Peripheral_zone_of_the_prostate'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df = data_new_df[data_new_df.SegmentedPropertyTypeCodeMeaning.isin(['Prostate', 'Prostatic structure'])]\n",
        "len(temp_df)"
      ],
      "metadata": {
        "id": "ol1vft1gdlUf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d512734e-016a-4589-9705-63bd7a14bf98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4459"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp2_df = data_new_df[data_new_df.idcSegmentCodeMeaning.isin(['Prostate'])]\n",
        "len(temp2_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otdF7x550O_D",
        "outputId": "a353dabc-a472-4244-f62f-bafac44b2568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4459"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df.collection_id.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KeggCxCK3cpT",
        "outputId": "67fd903d-76d1-454a-df2d-e6a673605174"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['prostatex', 'prostate_mri_us_biopsy',\n",
              "       'qin_prostate_repeatability'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ProstateX"
      ],
      "metadata": {
        "id": "wWytXprb4Ybx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostatex = temp_df[temp_df[\"collection_id\"]==\"prostatex\"].groupby('SegmentAlgorithmName').agg(\n",
        "    mean_dice=('dsc', 'mean'),\n",
        "    std_dice=('dsc', 'std'),\n",
        "    min_dice=('dsc', 'min'),\n",
        "    max_dice=('dsc', 'max'),\n",
        "    mean_hausdorff95=('hsdff_95', 'mean'),\n",
        "    std_hausdorff95=('hsdff_95', 'std'),\n",
        "    min_hausdorff95=('hsdff_95', 'min'),\n",
        "    max_hausdorff95=('hsdff_95', 'max'),\n",
        "    mean_ai_volume=(\"ai_Value\", \"mean\"),\n",
        "    std_ai_volume=(\"ai_Value\", \"std\"),\n",
        "    min_ai_volume=('ai_Value', 'min'),\n",
        "    max_ai_volume=('ai_Value', 'max'),\n",
        "    mean_expert_volume=(\"expert_Value\", \"mean\"),\n",
        "    std_expert_volume=(\"expert_Value\", \"std\"),\n",
        "    min_expert_volume=('expert_Value', 'min'),\n",
        "    max_expert_volume=('expert_Value', 'max'),\n",
        "    num_samples=('dsc', 'size')  # Counting number of samples\n",
        ").round(2).reset_index()"
      ],
      "metadata": {
        "id": "MQyNIUBK24ad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostatex"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "Z8i1N_8M3LwT",
        "outputId": "4e31761e-7e80-4726-819c-9a9f47b6500d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SegmentAlgorithmName  mean_dice  std_dice  min_dice  max_dice  \\\n",
              "0       bamf_nnunet_mr_prostate       0.95      0.01      0.91      0.97   \n",
              "1             monai_prostate158       0.92      0.02      0.86      0.96   \n",
              "2        nnunet_prostate_task24       0.93      0.03      0.74      0.96   \n",
              "3  nnunet_prostate_zonal_task05       0.90      0.04      0.64      0.95   \n",
              "\n",
              "   mean_hausdorff95  std_hausdorff95  min_hausdorff95  max_hausdorff95  \\\n",
              "0              0.30             0.28             0.00             1.50   \n",
              "1              0.92             0.41             0.25             2.50   \n",
              "2              0.90             0.91             0.00             8.13   \n",
              "3              1.69             1.46             0.35            12.20   \n",
              "\n",
              "  mean_ai_volume  std_ai_volume min_ai_volume  max_ai_volume  \\\n",
              "0      67.710969          32.36  18.723625000  167.490781000   \n",
              "1      65.011331          30.70  19.729719000  161.858781000   \n",
              "2      67.971718          30.44  22.186250000  166.366906000   \n",
              "3      66.914626          30.10  20.170844000  163.664281000   \n",
              "\n",
              "  mean_expert_volume  std_expert_volume min_expert_volume max_expert_volume  \\\n",
              "0          66.392519              32.11      17.599844000     167.920156000   \n",
              "1          66.392519              32.11      17.599844000     167.920156000   \n",
              "2          66.392519              32.11      17.599844000     167.920156000   \n",
              "3          66.392519              32.11      17.599844000     167.920156000   \n",
              "\n",
              "   num_samples  \n",
              "0           98  \n",
              "1           98  \n",
              "2           98  \n",
              "3           98  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ed80aa62-99f8-481a-8e3d-e292cf0cd499\" 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>SegmentAlgorithmName</th>\n",
              "      <th>mean_dice</th>\n",
              "      <th>std_dice</th>\n",
              "      <th>min_dice</th>\n",
              "      <th>max_dice</th>\n",
              "      <th>mean_hausdorff95</th>\n",
              "      <th>std_hausdorff95</th>\n",
              "      <th>min_hausdorff95</th>\n",
              "      <th>max_hausdorff95</th>\n",
              "      <th>mean_ai_volume</th>\n",
              "      <th>std_ai_volume</th>\n",
              "      <th>min_ai_volume</th>\n",
              "      <th>max_ai_volume</th>\n",
              "      <th>mean_expert_volume</th>\n",
              "      <th>std_expert_volume</th>\n",
              "      <th>min_expert_volume</th>\n",
              "      <th>max_expert_volume</th>\n",
              "      <th>num_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bamf_nnunet_mr_prostate</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.50</td>\n",
              "      <td>67.710969</td>\n",
              "      <td>32.36</td>\n",
              "      <td>18.723625000</td>\n",
              "      <td>167.490781000</td>\n",
              "      <td>66.392519</td>\n",
              "      <td>32.11</td>\n",
              "      <td>17.599844000</td>\n",
              "      <td>167.920156000</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>monai_prostate158</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.25</td>\n",
              "      <td>2.50</td>\n",
              "      <td>65.011331</td>\n",
              "      <td>30.70</td>\n",
              "      <td>19.729719000</td>\n",
              "      <td>161.858781000</td>\n",
              "      <td>66.392519</td>\n",
              "      <td>32.11</td>\n",
              "      <td>17.599844000</td>\n",
              "      <td>167.920156000</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nnunet_prostate_task24</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.13</td>\n",
              "      <td>67.971718</td>\n",
              "      <td>30.44</td>\n",
              "      <td>22.186250000</td>\n",
              "      <td>166.366906000</td>\n",
              "      <td>66.392519</td>\n",
              "      <td>32.11</td>\n",
              "      <td>17.599844000</td>\n",
              "      <td>167.920156000</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nnunet_prostate_zonal_task05</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.95</td>\n",
              "      <td>1.69</td>\n",
              "      <td>1.46</td>\n",
              "      <td>0.35</td>\n",
              "      <td>12.20</td>\n",
              "      <td>66.914626</td>\n",
              "      <td>30.10</td>\n",
              "      <td>20.170844000</td>\n",
              "      <td>163.664281000</td>\n",
              "      <td>66.392519</td>\n",
              "      <td>32.11</td>\n",
              "      <td>17.599844000</td>\n",
              "      <td>167.920156000</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
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            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "QIN"
      ],
      "metadata": {
        "id": "RxnqhVk74qtD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_qin = temp_df[temp_df[\"collection_id\"]==\"qin_prostate_repeatability\"].groupby('SegmentAlgorithmName').agg(\n",
        "    mean_dice=('dsc', 'mean'),\n",
        "    std_dice=('dsc', 'std'),\n",
        "    min_dice=('dsc', 'min'),\n",
        "    max_dice=('dsc', 'max'),\n",
        "    mean_hausdorff95=('hsdff_95', 'mean'),\n",
        "    std_hausdorff95=('hsdff_95', 'std'),\n",
        "    min_hausdorff95=('hsdff_95', 'min'),\n",
        "    max_hausdorff95=('hsdff_95', 'max'),\n",
        "    mean_ai_volume=(\"ai_Value\", \"mean\"),\n",
        "    std_ai_volume=(\"ai_Value\", \"std\"),\n",
        "    min_ai_volume=('ai_Value', 'min'),\n",
        "    max_ai_volume=('ai_Value', 'max'),\n",
        "    mean_expert_volume=(\"expert_Value\", \"mean\"),\n",
        "    std_expert_volume=(\"expert_Value\", \"std\"),\n",
        "    min_expert_volume=('expert_Value', 'min'),\n",
        "    max_expert_volume=('expert_Value', 'max'),\n",
        "    num_samples=('dsc', 'size')  # Counting number of samples\n",
        ").round(2).reset_index()"
      ],
      "metadata": {
        "id": "O3JD00RK4qtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_qin"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "3578bd69-8861-42bd-bf88-b4e928dcbcd9",
        "id": "v52_quit4qtE"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SegmentAlgorithmName  mean_dice  std_dice  min_dice  max_dice  \\\n",
              "0       bamf_nnunet_mr_prostate       0.79      0.17      0.19      0.92   \n",
              "1             monai_prostate158       0.83      0.05      0.70      0.90   \n",
              "2        nnunet_prostate_task24       0.85      0.05      0.72      0.92   \n",
              "3  nnunet_prostate_zonal_task05       0.57      0.27      0.00      0.87   \n",
              "\n",
              "   mean_hausdorff95  std_hausdorff95  min_hausdorff95  max_hausdorff95  \\\n",
              "0              4.73             4.74             0.62            18.18   \n",
              "1              3.55             2.38             1.50            12.07   \n",
              "2              2.60             1.93             1.09             9.98   \n",
              "3             14.82            12.88             1.81            56.11   \n",
              "\n",
              "  mean_ai_volume  std_ai_volume min_ai_volume  max_ai_volume  \\\n",
              "0      39.983223          19.71   3.877832000   96.642958000   \n",
              "1      52.783244          29.91  23.120300000  124.452408000   \n",
              "2      54.373543          27.91  25.274320000  111.598841000   \n",
              "3       42.76282          22.83   0.169168000   88.113334000   \n",
              "\n",
              "  mean_expert_volume  std_expert_volume min_expert_volume max_expert_volume  \\\n",
              "0          46.450388              28.11      19.038892000     115.874483000   \n",
              "1          46.450388              28.11      19.038892000     115.874483000   \n",
              "2          46.450388              28.11      19.038892000     115.874483000   \n",
              "3          46.450388              28.11      19.038892000     115.874483000   \n",
              "\n",
              "   num_samples  \n",
              "0           30  \n",
              "1           30  \n",
              "2           30  \n",
              "3           30  "
            ],
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              "      <th>SegmentAlgorithmName</th>\n",
              "      <th>mean_dice</th>\n",
              "      <th>std_dice</th>\n",
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              "      <th>min_ai_volume</th>\n",
              "      <th>max_ai_volume</th>\n",
              "      <th>mean_expert_volume</th>\n",
              "      <th>std_expert_volume</th>\n",
              "      <th>min_expert_volume</th>\n",
              "      <th>max_expert_volume</th>\n",
              "      <th>num_samples</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bamf_nnunet_mr_prostate</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.92</td>\n",
              "      <td>4.73</td>\n",
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              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>monai_prostate158</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.90</td>\n",
              "      <td>3.55</td>\n",
              "      <td>2.38</td>\n",
              "      <td>1.50</td>\n",
              "      <td>12.07</td>\n",
              "      <td>52.783244</td>\n",
              "      <td>29.91</td>\n",
              "      <td>23.120300000</td>\n",
              "      <td>124.452408000</td>\n",
              "      <td>46.450388</td>\n",
              "      <td>28.11</td>\n",
              "      <td>19.038892000</td>\n",
              "      <td>115.874483000</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nnunet_prostate_task24</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1.93</td>\n",
              "      <td>1.09</td>\n",
              "      <td>9.98</td>\n",
              "      <td>54.373543</td>\n",
              "      <td>27.91</td>\n",
              "      <td>25.274320000</td>\n",
              "      <td>111.598841000</td>\n",
              "      <td>46.450388</td>\n",
              "      <td>28.11</td>\n",
              "      <td>19.038892000</td>\n",
              "      <td>115.874483000</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nnunet_prostate_zonal_task05</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.87</td>\n",
              "      <td>14.82</td>\n",
              "      <td>12.88</td>\n",
              "      <td>1.81</td>\n",
              "      <td>56.11</td>\n",
              "      <td>42.76282</td>\n",
              "      <td>22.83</td>\n",
              "      <td>0.169168000</td>\n",
              "      <td>88.113334000</td>\n",
              "      <td>46.450388</td>\n",
              "      <td>28.11</td>\n",
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              "      <td>115.874483000</td>\n",
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              "variable_name": "metrics_summary_qin",
              "summary": "{\n  \"name\": \"metrics_summary_qin\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SegmentAlgorithmName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"monai_prostate158\",\n          \"nnunet_prostate_zonal_task05\",\n          \"bamf_nnunet_mr_prostate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12909944487358058,\n        \"min\": 0.57,\n        \"max\": 0.85,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.83,\n          0.57,\n          0.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1063014581273465,\n        \"min\": 0.05,\n        \"max\": 0.27,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.17,\n          0.05,\n          0.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.363535876267162,\n        \"min\": 0.0,\n        \"max\": 0.72,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.7,\n          0.0,\n          0.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02362907813126306,\n        \"min\": 0.87,\n        \"max\": 0.92,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.92,\n          0.9,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.664076859177201,\n        \"min\": 2.6,\n        \"max\": 14.82,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.55,\n          14.82,\n          4.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.0833084698845505,\n        \"min\": 1.93,\n        \"max\": 12.88,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.38,\n          12.88,\n          4.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5159134294304294,\n        \"min\": 0.62,\n        \"max\": 1.81,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.5,\n          1.81,\n          0.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.631619603410805,\n        \"min\": 9.98,\n        \"max\": 56.11,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12.07,\n          56.11,\n          18.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 39.983223100000004,\n        \"max\": 54.373543000000005,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          52.783244366666665,\n          42.7628202,\n          39.983223100000004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.66321777316908,\n        \"min\": 19.71,\n        \"max\": 29.91,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          29.91,\n          22.83,\n          19.71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"23.120300000\",\n          \"0.169168000\",\n          \"3.877832000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"124.452408000\",\n          \"88.113334000\",\n          \"96.642958000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 46.4503882,\n        \"max\": 46.4503882,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          46.4503882\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 28.11,\n        \"max\": 28.11,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          28.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"19.038892000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"115.874483000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_samples\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 30,\n        \"max\": 30,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prostate-MRI-US-Biopsy"
      ],
      "metadata": {
        "id": "kgvpdFNF42Jb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostate_mri_us_biopsy = temp_df[temp_df[\"collection_id\"]==\"prostate_mri_us_biopsy\"].groupby('SegmentAlgorithmName').agg(\n",
        "    mean_dice=('dsc', 'mean'),\n",
        "    std_dice=('dsc', 'std'),\n",
        "    min_dice=('dsc', 'min'),\n",
        "    max_dice=('dsc', 'max'),\n",
        "    mean_hausdorff95=('hsdff_95', 'mean'),\n",
        "    std_hausdorff95=('hsdff_95', 'std'),\n",
        "    min_hausdorff95=('hsdff_95', 'min'),\n",
        "    max_hausdorff95=('hsdff_95', 'max'),\n",
        "    mean_ai_volume=(\"ai_Value\", \"mean\"),\n",
        "    std_ai_volume=(\"ai_Value\", \"std\"),\n",
        "    min_ai_volume=('ai_Value', 'min'),\n",
        "    max_ai_volume=('ai_Value', 'max'),\n",
        "    mean_expert_volume=(\"expert_Value\", \"mean\"),\n",
        "    std_expert_volume=(\"expert_Value\", \"std\"),\n",
        "    min_expert_volume=('expert_Value', 'min'),\n",
        "    max_expert_volume=('expert_Value', 'max'),\n",
        "    num_samples=('dsc', 'size')  # Counting number of samples\n",
        ").round(2).reset_index()"
      ],
      "metadata": {
        "id": "ZcFoV58t42Jc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostate_mri_us_biopsy.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "7eb09879-be10-4294-f8d7-be8d0ed62a18",
        "id": "91HVL1xP42Jc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SegmentAlgorithmName  mean_dice  std_dice  min_dice  max_dice  \\\n",
              "0       bamf_nnunet_mr_prostate       0.89      0.05      0.13      0.96   \n",
              "1             monai_prostate158       0.88      0.07      0.12      0.95   \n",
              "2        nnunet_prostate_task24       0.86      0.09      0.10      0.95   \n",
              "3  nnunet_prostate_zonal_task05       0.79      0.16      0.00      0.94   \n",
              "\n",
              "   mean_hausdorff95  std_hausdorff95  min_hausdorff95  max_hausdorff95  \\\n",
              "0              1.54             1.66             0.00            24.93   \n",
              "1              1.77             2.61             0.33            34.99   \n",
              "2              2.94             6.53             0.33            56.04   \n",
              "3              9.17            14.33             0.47            63.17   \n",
              "\n",
              "  mean_ai_volume  std_ai_volume min_ai_volume  max_ai_volume  \\\n",
              "0      50.928355          26.32  10.166936000  230.466186000   \n",
              "1      47.462448          24.06   8.067497000  193.951943000   \n",
              "2      51.524569          22.20  19.457620000  202.835437000   \n",
              "3      50.593193          22.60   7.370234000  215.524546000   \n",
              "\n",
              "  mean_expert_volume  std_expert_volume min_expert_volume max_expert_volume  \\\n",
              "0          48.966954              26.11      10.563183000     222.405063000   \n",
              "1          48.958933              26.10      10.563183000     222.405063000   \n",
              "2          48.958933              26.10      10.563183000     222.405063000   \n",
              "3          48.958933              26.10      10.563183000     222.405063000   \n",
              "\n",
              "   num_samples  \n",
              "0          986  \n",
              "1          987  \n",
              "2          987  \n",
              "3          987  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SegmentAlgorithmName</th>\n",
              "      <th>mean_dice</th>\n",
              "      <th>std_dice</th>\n",
              "      <th>min_dice</th>\n",
              "      <th>max_dice</th>\n",
              "      <th>mean_hausdorff95</th>\n",
              "      <th>std_hausdorff95</th>\n",
              "      <th>min_hausdorff95</th>\n",
              "      <th>max_hausdorff95</th>\n",
              "      <th>mean_ai_volume</th>\n",
              "      <th>std_ai_volume</th>\n",
              "      <th>min_ai_volume</th>\n",
              "      <th>max_ai_volume</th>\n",
              "      <th>mean_expert_volume</th>\n",
              "      <th>std_expert_volume</th>\n",
              "      <th>min_expert_volume</th>\n",
              "      <th>max_expert_volume</th>\n",
              "      <th>num_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bamf_nnunet_mr_prostate</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.96</td>\n",
              "      <td>1.54</td>\n",
              "      <td>1.66</td>\n",
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              "      <td>24.93</td>\n",
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              "      <td>26.32</td>\n",
              "      <td>10.166936000</td>\n",
              "      <td>230.466186000</td>\n",
              "      <td>48.966954</td>\n",
              "      <td>26.11</td>\n",
              "      <td>10.563183000</td>\n",
              "      <td>222.405063000</td>\n",
              "      <td>986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>monai_prostate158</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.95</td>\n",
              "      <td>1.77</td>\n",
              "      <td>2.61</td>\n",
              "      <td>0.33</td>\n",
              "      <td>34.99</td>\n",
              "      <td>47.462448</td>\n",
              "      <td>24.06</td>\n",
              "      <td>8.067497000</td>\n",
              "      <td>193.951943000</td>\n",
              "      <td>48.958933</td>\n",
              "      <td>26.10</td>\n",
              "      <td>10.563183000</td>\n",
              "      <td>222.405063000</td>\n",
              "      <td>987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nnunet_prostate_task24</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.95</td>\n",
              "      <td>2.94</td>\n",
              "      <td>6.53</td>\n",
              "      <td>0.33</td>\n",
              "      <td>56.04</td>\n",
              "      <td>51.524569</td>\n",
              "      <td>22.20</td>\n",
              "      <td>19.457620000</td>\n",
              "      <td>202.835437000</td>\n",
              "      <td>48.958933</td>\n",
              "      <td>26.10</td>\n",
              "      <td>10.563183000</td>\n",
              "      <td>222.405063000</td>\n",
              "      <td>987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nnunet_prostate_zonal_task05</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>9.17</td>\n",
              "      <td>14.33</td>\n",
              "      <td>0.47</td>\n",
              "      <td>63.17</td>\n",
              "      <td>50.593193</td>\n",
              "      <td>22.60</td>\n",
              "      <td>7.370234000</td>\n",
              "      <td>215.524546000</td>\n",
              "      <td>48.958933</td>\n",
              "      <td>26.10</td>\n",
              "      <td>10.563183000</td>\n",
              "      <td>222.405063000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "metrics_summary_prostate_mri_us_biopsy",
              "summary": "{\n  \"name\": \"metrics_summary_prostate_mri_us_biopsy\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SegmentAlgorithmName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"monai_prostate158\",\n          \"nnunet_prostate_zonal_task05\",\n          \"bamf_nnunet_mr_prostate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04509249752822893,\n        \"min\": 0.79,\n        \"max\": 0.89,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.88,\n          0.79,\n          0.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04787135538781691,\n        \"min\": 0.05,\n        \"max\": 0.16,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.07,\n          0.16,\n          0.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.059651767227244266,\n        \"min\": 0.0,\n        \"max\": 0.13,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.12,\n          0.0,\n          0.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008164965809277268,\n        \"min\": 0.94,\n        \"max\": 0.96,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.96,\n          0.95,\n          0.94\n        ],\n        \"semantic_type\": \"\",\n  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    \"230.466186000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 48.95893288855117,\n        \"max\": 48.966954209939146,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          48.95893288855117,\n          48.966954209939146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004999999999999005,\n        \"min\": 26.1,\n        \"max\": 26.11,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          26.1,\n          26.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"10.563183000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"222.405063000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_samples\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 986,\n        \"max\": 987,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          987\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZnWKVJkL28O"
      },
      "source": [
        "### Peripheral zone quantitative analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df = data_new_df[data_new_df.SegmentedPropertyTypeCodeMeaning == 'Structure of peripheral glandular zone of prostate (body structure)']"
      ],
      "metadata": {
        "id": "GIxhdNkLdoRZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostatex = temp_df[temp_df[\"collection_id\"]==\"prostatex\"].groupby('SegmentAlgorithmName').agg(\n",
        "    mean_dice=('dsc', 'mean'),\n",
        "    std_dice=('dsc', 'std'),\n",
        "    min_dice=('dsc', 'min'),\n",
        "    max_dice=('dsc', 'max'),\n",
        "    mean_hausdorff95=('hsdff_95', 'mean'),\n",
        "    std_hausdorff95=('hsdff_95', 'std'),\n",
        "    min_hausdorff95=('hsdff_95', 'min'),\n",
        "    max_hausdorff95=('hsdff_95', 'max'),\n",
        "    mean_ai_volume=(\"ai_Value\", \"mean\"),\n",
        "    std_ai_volume=(\"ai_Value\", \"std\"),\n",
        "    min_ai_volume=('ai_Value', 'min'),\n",
        "    max_ai_volume=('ai_Value', 'max'),\n",
        "    mean_expert_volume=(\"expert_Value\", \"mean\"),\n",
        "    std_expert_volume=(\"expert_Value\", \"std\"),\n",
        "    min_expert_volume=('expert_Value', 'min'),\n",
        "    max_expert_volume=('expert_Value', 'max'),\n",
        "    num_samples=('dsc', 'size')  # Counting number of samples\n",
        ").round(2).reset_index()"
      ],
      "metadata": {
        "id": "Hsn3JcJjPet0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostatex.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "-q85_XTpPhog",
        "outputId": "c3349269-8344-4038-ca0b-96c44b87f21b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SegmentAlgorithmName  mean_dice  std_dice  min_dice  max_dice  \\\n",
              "0             monai_prostate158       0.74      0.08      0.46      0.88   \n",
              "1  nnunet_prostate_zonal_task05       0.67      0.14      0.06      0.84   \n",
              "\n",
              "   mean_hausdorff95  std_hausdorff95  min_hausdorff95  max_hausdorff95  \\\n",
              "0              3.27             1.88             1.21            12.79   \n",
              "1              4.81             3.18             1.50            20.07   \n",
              "\n",
              "  mean_ai_volume  std_ai_volume min_ai_volume max_ai_volume  \\\n",
              "0      14.661519           5.30   5.763938000  33.002906000   \n",
              "1      12.938248           4.94   0.796156000  31.919719000   \n",
              "\n",
              "  mean_expert_volume  std_expert_volume min_expert_volume max_expert_volume  \\\n",
              "0          19.779091               6.98       6.547250000      42.050969000   \n",
              "1          19.799546               7.01       6.547250000      42.050969000   \n",
              "\n",
              "   num_samples  \n",
              "0           98  \n",
              "1           97  "
            ],
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              "    }\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-96a69a6c-7f29-4c1e-ab94-c61d021772d4 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": "metrics_summary_prostatex",
              "summary": "{\n  \"name\": \"metrics_summary_prostatex\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"SegmentAlgorithmName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"nnunet_prostate_zonal_task05\",\n          \"monai_prostate158\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04949747468305829,\n        \"min\": 0.67,\n        \"max\": 0.74,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.67,\n          0.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04242640687119286,\n        \"min\": 0.08,\n        \"max\": 0.14,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.14,\n          0.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28284271247461906,\n        \"min\": 0.06,\n        \"max\": 0.46,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.06,\n          0.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.028284271247461926,\n        \"min\": 0.84,\n        \"max\": 0.88,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.84,\n          0.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0889444430272828,\n        \"min\": 3.27,\n        \"max\": 4.81,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.81,\n          3.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.919238815542512,\n        \"min\": 1.88,\n        \"max\": 3.18,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.18,\n          1.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2050609665440988,\n        \"min\": 1.21,\n        \"max\": 1.5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.5,\n          1.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_hausdorff95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.147737367038067,\n        \"min\": 12.79,\n        \"max\": 20.07,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          20.07,\n          12.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 12.938248175257732,\n        \"max\": 14.661518959183674,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          12.938248175257732,\n          14.661518959183674\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2545584412271567,\n        \"min\": 4.94,\n        \"max\": 5.3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.94,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0.796156000\",\n          \"5.763938000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_ai_volume\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"31.919719000\",\n          \"33.002906000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 19.77909106122449,\n        \"max\": 19.79954592783505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          19.79954592783505,\n          19.77909106122449\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021213203435595972,\n        \"min\": 6.98,\n        \"max\": 7.01,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7.01,\n          6.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"6.547250000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_expert_volume\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"42.050969000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_samples\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 97,\n        \"max\": 98,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_qin = temp_df[temp_df[\"collection_id\"]==\"qin_prostate_repeatability\"].groupby('SegmentAlgorithmName').agg(\n",
        "    mean_dice=('dsc', 'mean'),\n",
        "    std_dice=('dsc', 'std'),\n",
        "    min_dice=('dsc', 'min'),\n",
        "    max_dice=('dsc', 'max'),\n",
        "    mean_hausdorff95=('hsdff_95', 'mean'),\n",
        "    std_hausdorff95=('hsdff_95', 'std'),\n",
        "    min_hausdorff95=('hsdff_95', 'min'),\n",
        "    max_hausdorff95=('hsdff_95', 'max'),\n",
        "    mean_ai_volume=(\"ai_Value\", \"mean\"),\n",
        "    std_ai_volume=(\"ai_Value\", \"std\"),\n",
        "    min_ai_volume=('ai_Value', 'min'),\n",
        "    max_ai_volume=('ai_Value', 'max'),\n",
        "    mean_expert_volume=(\"expert_Value\", \"mean\"),\n",
        "    std_expert_volume=(\"expert_Value\", \"std\"),\n",
        "    min_expert_volume=('expert_Value', 'min'),\n",
        "    max_expert_volume=('expert_Value', 'max'),\n",
        "    num_samples=('dsc', 'size')  # Counting number of samples\n",
        ").round(2).reset_index()"
      ],
      "metadata": {
        "id": "Xdw1Kz-TQuiL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_qin.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "pe-7PCAfQyVD",
        "outputId": "0cc3fe5c-b406-4fb6-8d8a-341895391d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SegmentAlgorithmName  mean_dice  std_dice  min_dice  max_dice  \\\n",
              "0             monai_prostate158       0.65      0.16      0.15      0.79   \n",
              "1  nnunet_prostate_zonal_task05       0.39      0.30      0.00      0.80   \n",
              "\n",
              "   mean_hausdorff95  std_hausdorff95  min_hausdorff95  max_hausdorff95  \\\n",
              "0              5.48             4.56             1.88            20.88   \n",
              "1             18.37            23.40             2.49            97.66   \n",
              "\n",
              "  mean_ai_volume  std_ai_volume min_ai_volume max_ai_volume  \\\n",
              "0      11.147744           3.15   5.128125000  17.695422000   \n",
              "1       6.754592           5.51   0.005930000  15.105507000   \n",
              "\n",
              "  mean_expert_volume  std_expert_volume min_expert_volume max_expert_volume  \\\n",
              "0             9.8965               3.98       3.202537000      18.637438000   \n",
              "1          10.063348               3.94       3.202537000      18.637438000   \n",
              "\n",
              "   num_samples  \n",
              "0           30  \n",
              "1           29  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SegmentAlgorithmName</th>\n",
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              "      <th>0</th>\n",
              "      <td>monai_prostate158</td>\n",
              "      <td>0.65</td>\n",
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              "      <th>1</th>\n",
              "      <td>nnunet_prostate_zonal_task05</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.80</td>\n",
              "      <td>18.37</td>\n",
              "      <td>23.40</td>\n",
              "      <td>2.49</td>\n",
              "      <td>97.66</td>\n",
              "      <td>6.754592</td>\n",
              "      <td>5.51</td>\n",
              "      <td>0.005930000</td>\n",
              "      <td>15.105507000</td>\n",
              "      <td>10.063348</td>\n",
              "      <td>3.94</td>\n",
              "      <td>3.202537000</td>\n",
              "      <td>18.637438000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "metrics_summary_qin",
              "summary": "{\n  \"name\": \"metrics_summary_qin\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"SegmentAlgorithmName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"nnunet_prostate_zonal_task05\",\n          \"monai_prostate158\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18384776310850237,\n        \"min\": 0.39,\n        \"max\": 0.65,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.39,\n          0.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_dice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09899494936611665,\n        \"min\": 0.16,\n        \"max\": 0.3,\n       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            }
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9WOuZw8L28O"
      },
      "source": [
        "### Transition zone quantitative analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df = data_new_df[data_new_df.SegmentedPropertyTypeCodeMeaning == 'Structure of transition zone of prostate (body structure)']"
      ],
      "metadata": {
        "id": "YLN6vmA4dqga"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostatex = temp_df[temp_df[\"collection_id\"]==\"prostatex\"].groupby('SegmentAlgorithmName').agg(\n",
        "    mean_dice=('dsc', 'mean'),\n",
        "    std_dice=('dsc', 'std'),\n",
        "    min_dice=('dsc', 'min'),\n",
        "    max_dice=('dsc', 'max'),\n",
        "    mean_hausdorff95=('hsdff_95', 'mean'),\n",
        "    std_hausdorff95=('hsdff_95', 'std'),\n",
        "    min_hausdorff95=('hsdff_95', 'min'),\n",
        "    max_hausdorff95=('hsdff_95', 'max'),\n",
        "    mean_ai_volume=(\"ai_Value\", \"mean\"),\n",
        "    std_ai_volume=(\"ai_Value\", \"std\"),\n",
        "    min_ai_volume=('ai_Value', 'min'),\n",
        "    max_ai_volume=('ai_Value', 'max'),\n",
        "    mean_expert_volume=(\"expert_Value\", \"mean\"),\n",
        "    std_expert_volume=(\"expert_Value\", \"std\"),\n",
        "    min_expert_volume=('expert_Value', 'min'),\n",
        "    max_expert_volume=('expert_Value', 'max'),\n",
        "    num_samples=('dsc', 'size')  # Counting number of samples\n",
        ").round(2).reset_index()"
      ],
      "metadata": {
        "id": "A0V9DhSDSUrN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_summary_prostatex.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "eWejvZ1HSWIV",
        "outputId": "3257f91c-48e1-438a-b0e9-3e783cc2892a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SegmentAlgorithmName  mean_dice  std_dice  min_dice  max_dice  \\\n",
              "0             monai_prostate158       0.85      0.07      0.59      0.94   \n",
              "1  nnunet_prostate_zonal_task05       0.82      0.11      0.35      0.94   \n",
              "\n",
              "   mean_hausdorff95  std_hausdorff95  min_hausdorff95  max_hausdorff95  \\\n",
              "0              2.17             1.06             0.35             5.29   \n",
              "1              2.96             2.28             0.75            15.83   \n",
              "\n",
              "  mean_ai_volume  std_ai_volume min_ai_volume  max_ai_volume  \\\n",
              "0      49.950719          29.56  10.056750000  145.559812000   \n",
              "1      54.021906          29.65  12.217750000  149.196125000   \n",
              "\n",
              "  mean_expert_volume  std_expert_volume min_expert_volume max_expert_volume  \\\n",
              "0          45.192203              30.09       6.134531000     144.135031000   \n",
              "1          45.192203              30.09       6.134531000     144.135031000   \n",
              "\n",
              "   num_samples  \n",
              "0           98  \n",
              "1           98  "
            ],
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              "      <td>0.82</td>\n",
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