terra_mhub/terra_data_table_setup_tsv_all.ipynb

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            "align_content": null,
            "align_items": null,
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            "border": null,
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            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
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            "margin": null,
            "max_height": null,
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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Env setup and google auth"
      ],
      "metadata": {
        "id": "gSlU9R0H9C-F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ],
      "metadata": {
        "id": "A1Dli2cuauoF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install lz4"
      ],
      "metadata": {
        "id": "xN_sIp5Daxcn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fb3233f-186a-4db3-85f9-5696661b2d95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "lz4 is already the newest version (1.9.3-2build2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyyaml s5cmd pydicom"
      ],
      "metadata": {
        "id": "cDSss6JAdTFQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee2eb70d-711c-4d96-dc3e-4e39ea8e9d86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (6.0.2)\n",
            "Requirement already satisfied: s5cmd in /usr/local/lib/python3.10/dist-packages (0.2.0)\n",
            "Requirement already satisfied: pydicom in /usr/local/lib/python3.10/dist-packages (3.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Python imports"
      ],
      "metadata": {
        "id": "j8Xi8X0u9J7_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "import yaml\n",
        "import math\n",
        "import pandas as pd\n",
        "import json"
      ],
      "metadata": {
        "id": "cWiNKEKPrQdx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Global variables"
      ],
      "metadata": {
        "id": "WlNU2BhoEOmz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Terra data table default column scheme"
      ],
      "metadata": {
        "id": "SkRQD7VHERPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_data_columns = [\"default_id\",\n",
        "#OUTPUT ARCHIVE FILES -- leave empty, terra will fill these values\n",
        "'mhubCompressedOutputFile',\n",
        "'evalCompressedOutputFile',\n",
        "'radsAiCompressedOutputFile',\n",
        "'idcExpertCompressedOutputFile',\n",
        "'radsIdcExpertCompressedOutputFile',\n",
        "'finalCompressedOutputFile',\n",
        "#Resampling scheme\n",
        "'res_scheme_format',\n",
        "#AI EVAL -- which segments from AI SEGS do we want to evaluate?\n",
        "'dicomAiCodeValuesEval_lst',\n",
        "'dicomAiCodeMeaningEval_lst',\n",
        "'dicomAiCodingSchemeDesignatorEval_lst',\n",
        "#IDC EVAL -- which expert annotations SEGs segments?\n",
        "'dicomIdcCodeValuesEval_lst',\n",
        "'dicomIdcCodeMeaningEval_lst',\n",
        "'dicomIdcCodingSchemeDesignatorEval_lst',\n",
        "##IDC EVAL -- second set of annotations SEGs\n",
        "'dicomIdcAddCodeValuesEval_lst',\n",
        "'dicomIdcAddCodeMeaningEval_lst',\n",
        "'dicomIdcAddCodingSchemeDesignatorEval_lst',\n",
        "#Combination -- which DICOM values characterize whole prostate gland?\n",
        "'dicomCodeValuesProstate_lst',\n",
        "'dicomCodeMeaningProstate_lst',\n",
        "'dicomCodingSchemeDesignatorProstate_lst',\n",
        "#Radiomics\n",
        "##AI -- which AI SEGs segments to compute radiomics into SR DICOM?\n",
        "'dicomSrAiCodeValues_lst',\n",
        "'dicomSrAiCodeMeaning_lst',\n",
        "'dicomSrAiCodingSchemeDesignator_lst',\n",
        "##IDC -- which IDC expert SEGs segments to compute radiomics into SR DICOM?\n",
        "'dicomSrIdcCodeValues_lst',\n",
        "'dicomSrIdcCodeMeaning_lst',\n",
        "'dicomSrIdcCodingSchemeDesignator_lst',\n",
        "#MHUB -- models to run inferene from, and associated config files\n",
        "'mhub_model_name_lst',\n",
        "'mhubai_custom_config_lst',\n",
        "'mhubaiCustomSegmentAlgorithmName_lst',#custom SegmentAlgorithm lists for each mhub run model, define as ['','','','] if not desired\n",
        "#IDC serieUIDs parameters -- which (images,idc_seg_experts) from IDC to run\n",
        "'collection_id',\n",
        "'idcSegSeriesInstanceUIDs',\n",
        "'idcAddSegSeriesInstancceUIDs',\n",
        "'seriesInstanceUIDs',\n",
        "'adcSeriesInstanceUIDs']"
      ],
      "metadata": {
        "id": "vRfLu4YvETp-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Terra parameters per collection basis"
      ],
      "metadata": {
        "id": "PGk4Om6tGf6u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prostate-MRI-US-Biopsy"
      ],
      "metadata": {
        "id": "nB2IDFXaGkL8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_table_id_prostate_mri_us_biopsy = \"terra_mhub_prostate_mri_us_biopsy\"\n",
        "terra_prostate_mri_us_biopsy_values = [json.dumps(\"\"),#mhubCompressedOutputFile\n",
        "      json.dumps(\"\"),#evalCompressedOutputFile\n",
        "      json.dumps(\"\"),#radsAiCompressedOutputFile\n",
        "      json.dumps(\"\"),#idcExpertCompressedOutputFile\n",
        "      json.dumps(\"\"),#radsIdcExpertCompressedOutputFile\n",
        "      json.dumps(\"\"),#finalCompressedOutputFile\n",
        "      # json.dumps(\"nrrd\"),#res_scheme_format\n",
        "      \"nrrd\",\n",
        "      json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomAiCodeValuesEval_lst\n",
        "      json.dumps([\"Prostatic_structure\",\n",
        "                \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "                \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomAiCodeMeaningEval_lst\n",
        "      json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomAiCodingSchemeDesignatorEval_lst\n",
        "      json.dumps([\"41216001\", \"None\", \"None\"]),#dicomIdcCodeValuesEval_lst\n",
        "      json.dumps([\"Prostate\", \"None\", \"None\"]),#dicomIdcCodeMeaningEval_lst\n",
        "      json.dumps([\"SCT\", \"None\", \"None\"]),#dicomIdcCodingSchemeDesignatorEval_lst\n",
        "      json.dumps(\"\"),#dicomIdcAddCodeValuesEval_lst\n",
        "      json.dumps(\"\"),#dicomIdcAddCodeMeaningEval_lst\n",
        "      json.dumps(\"\"),#dicomIdcAddCodingSchemeDesignatorEval_lst\n",
        "      json.dumps([\"41216001\", \"T-9200B\"]),#dicomCodeValuesProstate_lst\n",
        "      json.dumps([\"Prostatic_structure\", \"Prostate\"]),#dicomCodeMeaningProstate_lst\n",
        "      json.dumps([\"SCT\", \"SRT\"]),#dicomCodingSchemeDesignatorProstate_lst\n",
        "      json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomSrAiCodeValues_lst\n",
        "      json.dumps([\"Prostatic_structure\",\n",
        "                \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "                \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomSrAiCodeMeaning_lst\n",
        "      json.dumps([\"SCT\",\n",
        "                \"SCT\",\n",
        "                \"SCT\"]),#dicomSrAiCodingSchemeDesignator_lst\n",
        "      json.dumps([\"41216001\"]),#dicomSrIdcCodeValues_lst\n",
        "      json.dumps([\"Prostate\"]),#dicomSrIdcCodeMeaning_lst\n",
        "      json.dumps([\"SCT\"]),#dicomSrIdcCodingSchemeDesignator_lst\n",
        "      json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                  \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhub_model_name_lst\n",
        "      json.dumps([\"default\", \"default\", \"default\", \"default\"]),#mhubai_custom_config_lst\n",
        "      json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                  \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhubaiCustomSegmentAlgorithmName_lst,\n",
        "      json.dumps(\"prostate_mri_us_biopsy\"),\n",
        "      # json.dumps(idc_seg_batch),#idcSegSeriesInstanceUIDs\n",
        "      # json.dumps(\"\"),#idcAddSegSeriesInstanceUIDs\n",
        "      # json.dumps(image_seg_batch),#t2_seriesInstanceUIDs\n",
        "      # json.dumps(image_adc_seg_batch),#adc_seriesInstanceUIDs\n",
        "      ]"
      ],
      "metadata": {
        "id": "Uh_jWaxaHdkB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ProstateX"
      ],
      "metadata": {
        "id": "nzCJTiRuGmao"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Parameters for studies that have both zonal and whole prostate SEGs DICOM"
      ],
      "metadata": {
        "id": "yUTMVfFCWixS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_table_id_prostatex_zonal_whole = \"terra_mhub_prostatex_zonal_whole_test\"\n",
        "terra_prostatex_zonal_whole_values =[json.dumps(\"\"),#mhubCompressedOutputFile\n",
        "    json.dumps(\"\"),#evalCompressedOutputFile\n",
        "    json.dumps(\"\"),#radsAiCompressedOutputFile\n",
        "    json.dumps(\"\"),#idcExpertCompressedOutputFile\n",
        "    json.dumps(\"\"),#radsIdcExpertCompressedOutputFile\n",
        "    json.dumps(\"\"),#finalCompressedOutputFile\n",
        "    # json.dumps(\"nii\"),#res_scheme_format\n",
        "    \"nii\",\n",
        "    json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomAiCodeValuesEval_lst\n",
        "    json.dumps([\"Prostatic_structure\",\n",
        "              \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "              \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomAiCodeMeaningEval_lst\n",
        "    json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomAiCodingSchemeDesignatorEval_lst\n",
        "    json.dumps([\"41216001\", \"None\", \"None\"]),#dicomIdcCodeValuesEval_lst\n",
        "    json.dumps([\"Prostate\", \"None\", \"None\"]),#dicomIdcCodeMeaningEval_lst\n",
        "    json.dumps([\"SCT\", \"None\", \"None\"]),#dicomIdcCodingSchemeDesignatorEval_lst\n",
        "    json.dumps([\"None\", \"279706003\", \"399384005\"]),#dicomIdcAddCodeValuesEval_lst\n",
        "    json.dumps([\"None\", \"Peripheral_zone_of_prostate\", \"Transition_zone_of_prostate\"]),#dicomIdcAddCodeMeaningEval_lst\n",
        "    json.dumps([\"None\", \"SCT\", \"SCT\"]),#dicomIdcAddCodingSchemeDesignatorEval_lst\n",
        "    json.dumps([\"41216001\", \"T-9200B\"]),#dicomCodeValuesProstate_lst\n",
        "    json.dumps([\"Prostatic_structure\", \"Prostate\"]),#dicomCodeMeaningProstate_lst\n",
        "    json.dumps([\"SCT\", \"SRT\"]),#dicomCodingSchemeDesignatorProstate_lst\n",
        "    json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomSrAiCodeValues_lst\n",
        "    json.dumps([\"Prostatic_structure\",\n",
        "              \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "              \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomSrAiCodeMeaning_lst\n",
        "    json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomSrAiCodingSchemeDesignator_lst\n",
        "    json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomSrIdcCodeValues_lst\n",
        "    json.dumps([\"Prostate\", \"Peripheral_zone_of_prostate\", \"Transition_zone_of_prostate\"]),#dicomSrIdcCodeMeaning_lst\n",
        "    json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomSrIdcCodingSchemeDesignator_lst\n",
        "    json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhub_model_name_lst\n",
        "    json.dumps([\"default\", \"default\",\n",
        "                \"default\", \"default\"]),#mhubai_custom_config_lst\n",
        "    json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                  \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhubaiCustomSegmentAlgorithmName_lst\n",
        "    json.dumps(\"prostatex_2_expert_seg_sets\"),\n",
        "    # json.dumps(idc_seg_batch),#idcSegSeriesInstanceUIDs\n",
        "    # json.dumps(idc_seg_add_batch),#idcAddSegSeriesInstanceUIDs\n",
        "    # json.dumps(image_seg_batch),#t2_seriesInstanceUIDs\n",
        "    # json.dumps(image_adc_seg_batch),#adc_seriesInstanceUIDs\n",
        "    ]"
      ],
      "metadata": {
        "id": "tklWqI5DVf1J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_table_id_prostatex_zonal_only = \"terra_mhub_prostatex_zonal_only_test\"\n",
        "terra_prostatex_zonal_only_values = [json.dumps(\"\"),#mhubCompressedOutputFile\n",
        "    json.dumps(\"\"),#evalCompressedOutputFile\n",
        "    json.dumps(\"\"),#radsAiCompressedOutputFile\n",
        "    json.dumps(\"\"),#idcExpertCompressedOutputFile\n",
        "    json.dumps(\"\"),#radsIdcExpertCompressedOutputFile\n",
        "    json.dumps(\"\"),#finalCompressedOutputFile\n",
        "    # json.dumps(\"nii\"),#res_scheme_format\n",
        "    \"nii\",\n",
        "    json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomAiCodeValuesEval_lst\n",
        "    json.dumps([\"Prostatic_structure\",\n",
        "              \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "              \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomAiCodeMeaningEval_lst\n",
        "    json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomAiCodingSchemeDesignatorEval_lst\n",
        "    json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomIdcCodeValuesEval_lst\n",
        "    json.dumps([\"Prostate\", \"Peripheral_zone_of_prostate\", \"Transition_zone_of_prostate\"]),#dicomIdcCodeMeaningEval_lst\n",
        "    json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomIdcCodingSchemeDesignatorEval_lst\n",
        "    json.dumps(\"\"),#dicomIdcAddCodeValuesEval_lst\n",
        "    json.dumps(\"\"),#dicomIdcAddCodeMeaningEval_lst\n",
        "    json.dumps(\"\"),#dicomIdcAddCodingSchemeDesignatorEval_lst\n",
        "    json.dumps([\"41216001\", \"T-9200B\"]),#dicomCodeValuesProstate_lst\n",
        "    json.dumps([\"Prostatic_structure\", \"Prostate\"]),#dicomCodeMeaningProstate_lst\n",
        "    json.dumps([\"SCT\", \"SRT\"]),#dicomCodingSchemeDesignatorProstate_lst\n",
        "    json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomSrAiCodeValues_lst\n",
        "    json.dumps([\"Prostatic_structure\",\n",
        "              \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "              \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomSrAiCodeMeaning_lst\n",
        "    json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomSrAiCodingSchemeDesignator_lst\n",
        "    json.dumps([\"41216001\", \"279706003\", \"399384005\"]),#dicomSrIdcCodeValues_lst\n",
        "    json.dumps([\"Prostate\", \"Peripheral_zone_of_prostate\", \"Transition_zone_of_prostate\"]),#dicomSrIdcCodeMeaning_lst\n",
        "    json.dumps([\"SCT\", \"SCT\", \"SCT\"]),#dicomSrIdcCodingSchemeDesignator_lst\n",
        "    json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhub_model_name_lst\n",
        "    json.dumps([\"default\", \"default\",\n",
        "                \"default\", \"default\"]),#mhubai_custom_config_lst,\n",
        "    json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                  \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhubaiCustomSegmentAlgorithmName_lst\n",
        "    json.dumps(\"prostatex_only_whole_prostate\"),\n",
        "    # json.dumps(idc_seg_batch),#idcSegSeriesInstanceUIDs\n",
        "    # json.dumps(\"\"),#idcAddSegSeriesInstanceUIDs\n",
        "    # json.dumps(image_seg_batch),#t2_seriesInstanceUIDs\n",
        "    # json.dumps(image_adc_seg_batch),#adc_seriesInstanceUIDs\n",
        "    ]"
      ],
      "metadata": {
        "id": "mcZO3kRhXEpC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### QIN-Prostate-Repeatability"
      ],
      "metadata": {
        "id": "ROd7CzvAGoz6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_table_id_qin_prostate_repeatability = \"terra_mhub_qin_prostate_repeatability_test\"\n",
        "terra_qin_prostate_repeatability_values = [json.dumps(\"\"),#mhubCompressedOutputFile\n",
        "    json.dumps(\"\"),#evalCompressedOutputFile\n",
        "    json.dumps(\"\"),#radsAiCompressedOutputFile\n",
        "    json.dumps(\"\"),#idcExpertCompressedOutputFile\n",
        "    json.dumps(\"\"),#radsIdcExpertCompressedOutputFile\n",
        "    json.dumps(\"\"),#finalCompressedOutputFile\n",
        "    # json.dumps(\"nii\"),#res_scheme_format\n",
        "    \"nii\",\n",
        "    json.dumps([\"41216001\",\n",
        "                \"279706003\",\n",
        "                \"399384005\"]),#dicomAiCodeValuesEval_lst\n",
        "    json.dumps([\"Prostatic_structure\",\n",
        "              \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "              \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomAiCodeMeaningEval_lst\n",
        "    json.dumps([\"SCT\",\"SCT\",\"SCT\"]),#dicomAiCodingSchemeDesignatorEval_lst\n",
        "    json.dumps([\"T-9200B\",\"T-D05E4\",\"None\"]),#dicomIdcCodeValuesEval_lst\n",
        "    json.dumps([\"Prostate\",\"Peripheral_zone_of_the_prostate\",\"None\"]),#dicomIdcCodeMeaningEval_lst\n",
        "    json.dumps([\"SRT\",\"SRT\",\"None\"]),#dicomIdcCodingSchemeDesignatorEval_lst\n",
        "    json.dumps(\"\"),#dicomIdcAddCodeValuesEval_lst\n",
        "    json.dumps(\"\"),#dicomIdcAddCodeMeaningEval_lst\n",
        "    json.dumps(\"\"),#dicomIdcAddCodingSchemeDesignatorEval_lst\n",
        "    json.dumps([\"41216001\", \"T-9200B\"]),#dicomCodeValuesProstate_lst\n",
        "    json.dumps([\"Prostatic_structure\", \"Prostate\"]),#dicomCodeMeaningProstate_lst\n",
        "    json.dumps([\"SCT\", \"SRT\"]),#dicomCodingSchemeDesignatorProstate_lst\n",
        "    json.dumps([\"41216001\",\n",
        "                \"279706003\",\n",
        "                \"399384005\"]),#dicomSrAiCodeValues_lst\n",
        "    json.dumps([\"Prostatic_structure\",\n",
        "              \"Structure_of_peripheral_glandular_zone_of_prostate_(body_structure)\",\n",
        "              \"Structure_of_transition_zone_of_prostate_(body_structure)\"]),#dicomSrAiCodeMeaning_lst\n",
        "    json.dumps([\"SCT\",\n",
        "              \"SCT\",\n",
        "              \"SCT\"]),#dicomSrAiCodingSchemeDesignator_lst\n",
        "    json.dumps([\"T-9200B\", \"T-D05E4\"]),#dicomSrIdcCodeValues_lst\n",
        "    json.dumps([\"Prostate\", \"Peripheral_zone_of_the_prostate\"]),#dicomSrIdcCodeMeaning_lst\n",
        "    json.dumps([\"SRT\", \"SRT\"]),#dicomSrIdcCodingSchemeDesignator_lst\n",
        "    json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhub_model_name_lst\n",
        "    json.dumps([\"default\", \"default\",\n",
        "                \"default\", \"default\"]),#mhubai_custom_config_lst\n",
        "    json.dumps([\"bamf_nnunet_mr_prostate\", \"nnunet_prostate_task24\",\n",
        "                  \"monai_prostate158\", \"nnunet_prostate_zonal_task05\"]),#mhubaiCustomSegmentAlgorithmName_lst\n",
        "    json.dumps(\"qin_prostate_repeatability\"),\n",
        "    # json.dumps(idc_seg_batch),#idcSegSeriesInstanceUIDs\n",
        "    # json.dumps(\"\"),#idcAddSegSeriesInstanceUIDs\n",
        "    # json.dumps(image_seg_batch),#t2_seriesInstanceUIDs\n",
        "    # json.dumps(image_adc_seg_batch),#adc_seriesInstanceUIDs\n",
        "    ]"
      ],
      "metadata": {
        "id": "VWIPswTQcKEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Function to build terra data table"
      ],
      "metadata": {
        "id": "4mfxDh_eEtk5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_data_table(idc_data_df,#idc dataframe containing T2 analyzed images serieUIDs and associated adc and expert seg seriesInstanceUIDs\n",
        "                     terra_col_scheme_lst, #terra data table column scheme\n",
        "                     terra_table_id, #every terra data table needs an id, such as 'PLACEHOLDER_id'\n",
        "                     terra_col_values_lst, #values associated to the column scheme\n",
        "                     idcSegColName,#t2 image df column name\n",
        "                     t2ImageColName,#adc image df column name\n",
        "                     adcImageColName,#idc expert seg df column name\n",
        "                     idcSegColNameAdd=None#idc expert seg second set df column name\n",
        "                     ):\n",
        "\n",
        "  # Set the number of rows per file\n",
        "  series_per_batch = 12\n",
        "\n",
        "  # Calculate the number of files needed\n",
        "  num_files = math.ceil(len(idc_data_df) / series_per_batch)\n",
        "\n",
        "  # Split the dataframe into multiple dataframes\n",
        "  dfs = [idc_data_df[i*series_per_batch:(i+1)*series_per_batch] for i in range(num_files)]\n",
        "\n",
        "  # Create a new column name for the batch_id column\n",
        "  batch_id_column = f'entity:{terra_table_id}_id'\n",
        "\n",
        "  # Initialize an empty DataFrame to store the batch information\n",
        "  out_batch_df = pd.DataFrame(columns=[batch_id_column]+terra_col_scheme_lst[1:])#first index is id\n",
        "\n",
        "  idc_seg_lst = []\n",
        "  image_seg_lst = []\n",
        "  image_adc_seg_lst = []\n",
        "  if idcSegColNameAdd is not None:#idc data df contains a col name with second set of expert SEG\n",
        "    idc_seg_add_lst = []\n",
        "    for batch_df in dfs:\n",
        "      idc_seg_lst.append(list(batch_df[idcSegColName].values))\n",
        "      idc_seg_add_lst.append(list(batch_df[idcSegColNameAdd].values))\n",
        "      image_seg_lst.append(list(batch_df[t2ImageColName].values))\n",
        "      image_adc_seg_lst.append(list(batch_df[adcImageColName].values))\n",
        "    idx=0\n",
        "    for image_seg_batch, idc_seg_batch, idc_seg_add_batch, image_adc_seg_batch in zip(image_seg_lst,idc_seg_lst, idc_seg_add_lst, image_adc_seg_lst):\n",
        "      out_batch_df.loc[idx] = [int(idx)] + terra_col_values_lst + [json.dumps(idc_seg_batch),\n",
        "          json.dumps(idc_seg_add_batch),\n",
        "          json.dumps(image_seg_batch),\n",
        "          json.dumps(image_adc_seg_batch)]\n",
        "      idx+=1\n",
        "    return out_batch_df\n",
        "  else:#no second set of EXPERT SEG\n",
        "    for batch_df in dfs:\n",
        "      idc_seg_lst.append(list(batch_df[idcSegColName].values))\n",
        "      image_seg_lst.append(list(batch_df[t2ImageColName].values))\n",
        "      image_adc_seg_lst.append(list(batch_df[adcImageColName].values))\n",
        "    idx=0\n",
        "    for image_seg_batch, idc_seg_batch, image_adc_seg_batch in zip(image_seg_lst, idc_seg_lst, image_adc_seg_lst):\n",
        "      out_batch_df.loc[idx] = [int(idx)] + terra_col_values_lst + [json.dumps(idc_seg_batch),\n",
        "          json.dumps(\"\"),\n",
        "          json.dumps(image_seg_batch),\n",
        "          json.dumps(image_adc_seg_batch)]\n",
        "      idx+=1\n",
        "    return out_batch_df"
      ],
      "metadata": {
        "id": "fecM-orfEvOf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Query IDC data\n",
        "\n",
        "Now that the model environment is set up, it's time to download some data.\n",
        "\n",
        "For our study, we are interested in retrieving, within individual studies and for one T2 image, one ADC image and associated T2 segmented expert prostate segmentations."
      ],
      "metadata": {
        "id": "YVvbdlAO8_g6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prostate-MRI-US-Biopsy collection"
      ],
      "metadata": {
        "id": "97cwzsMl9MpV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's query all T2W images from PROSTATE-MRI-US-BIOPSY that have both whole expert prostate SEG DCM objects"
      ],
      "metadata": {
        "id": "uOWnKG2ewIhO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery cohort_prostate_mri_us_biopsy_df --project idc-sandbox-003\n",
        "WITH\n",
        "  idc_seg_whole_prostate AS (\n",
        "  SELECT\n",
        "    segmentations.StudyInstanceUID,\n",
        "    segmentations.SeriesInstanceUID,\n",
        "    segmentations.segmented_SeriesInstanceUID,\n",
        "    # for debugging:\n",
        "    dicom_all.SeriesDescription\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.segmentations` AS segmentations\n",
        "  JOIN\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dicom_all\n",
        "  ON\n",
        "    segmentations.SeriesInstanceUID = dicom_all.SeriesInstanceUID\n",
        "  WHERE\n",
        "    dicom_all.analysis_result_id = \"Prostate-MRI-US-Biopsy-DICOM-Annotations\"\n",
        "    # the next line is equivalent, but less clear\n",
        "    #lower(dicom_all.source_doi) = \"10.5281/zenodo.10069910\"\n",
        "    AND segmentations.SegmentedPropertyType.CodeValue = '41216001'\n",
        "    AND segmentations.SegmentedPropertyType.CodingSchemeDesignator = 'SCT'\n",
        "    AND segmentations.SegmentAlgorithmType in UNNEST(['MANUAL', 'SEMIAUTOMATIC'])),\n",
        "  # select T2, here we do not check for multiple T2 within the same study, since we know there is only one per study\n",
        "  t2_series AS(\n",
        "  SELECT\n",
        "    DISTINCT dc_all.SeriesInstanceUID,\n",
        "    dc_all.StudyInstanceUID,\n",
        "    dc_all.PatientID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_all\n",
        "  WHERE\n",
        "    dc_all.collection_id = 'prostate_mri_us_biopsy'\n",
        "    AND dc_all.Modality = 'MR'\n",
        "    AND LOWER(dc_all.SeriesDescription) LIKE '%t2%'\n",
        "  ORDER BY\n",
        "    PatientID),\n",
        "  # ADC selection, but since we know there are studies with more than one, we select the latest\n",
        "  adc_series AS(\n",
        "  SELECT\n",
        "    dc_adc.StudyInstanceUID,\n",
        "    ARRAY_AGG(dc_adc.SeriesInstanceUID\n",
        "    ORDER BY\n",
        "      dc_adc.SeriesDate, dc_adc.SeriesTime DESC\n",
        "    LIMIT\n",
        "      1)[SAFE_OFFSET(0)] AS SeriesInstanceUID,\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_adc\n",
        "  JOIN\n",
        "    t2_series\n",
        "  ON\n",
        "    dc_adc.StudyInstanceUID = t2_series.StudyInstanceUID\n",
        "  WHERE\n",
        "    LOWER(dc_adc.SeriesDescription) LIKE '%adc%'\n",
        "  GROUP BY\n",
        "    dc_adc.StudyInstanceUID)\n",
        "SELECT\n",
        "  t2_series.SeriesInstanceUID AS t2_serieUID,\n",
        "  adc_series.SeriesInstanceUID AS adc_serieUID,\n",
        "  idc_seg_whole_prostate.SeriesInstanceUID AS expertWholeProstateSeriesInstanceUID,\n",
        "  idc_seg_whole_prostate.SeriesDescription AS expertWholeProstateSeriesDescription,\n",
        "  t2_series.StudyInstanceUID\n",
        "FROM\n",
        "  t2_series\n",
        "INNER JOIN\n",
        "  adc_series\n",
        "ON\n",
        "  t2_series.StudyInstanceUID = adc_series.StudyInstanceUID\n",
        "INNER JOIN\n",
        "  idc_seg_whole_prostate\n",
        "ON\n",
        "  t2_series.SeriesInstanceUID = idc_seg_whole_prostate.segmented_SeriesInstanceUID"
      ],
      "metadata": {
        "id": "stzgItorpsqZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "c9ff6c9c3cb0438db7ba75f1a8e0968e",
            "a771101dbf494236b40a6eeb8148bd26",
            "e9cf6be484dc4c47951c171da01394b4",
            "456d7465826d42fbb923dd1919c9af07",
            "36e41e3c29b84becb889afdd0efd1c6c",
            "f66100d43bd54bfda9eb383fd55121f6",
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            "77f3bf7d2f4040228a7d848ba621245c",
            "60950a427af4451ab14d24b7079f387c",
            "f681e9d8e8e14d2897d43f3976bddc57",
            "f411f57d36974c239ae35cffc1808784",
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            "fcb83a65e1c84d9e8fe69afa1c1cf85a",
            "aa0e70bc148c49b4b2a9a32118a4b8dc",
            "35d4aaa5590643e8a514a614f78c81ae"
          ]
        },
        "outputId": "032b1d11-cee7-4e98-ac55-906d9518a003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Query is running:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9ff6c9c3cb0438db7ba75f1a8e0968e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e4ad0a7c8ac4295bcb1c6b13e38eb41"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ProstateX collection"
      ],
      "metadata": {
        "id": "8EZ5zYfc9e8q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's query all T2W images from ProstateX that have both whole prostate and zonal expert SEG DCM objects -- n=66"
      ],
      "metadata": {
        "id": "VvZK8q_eBiM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery cohort_prostatex_zonal_whole_df --project idc-sandbox-003\n",
        "WITH\n",
        "  idc_seg_zonal_prostate AS(\n",
        "  SELECT\n",
        "    DISTINCT segmentations.SeriesInstanceUID,\n",
        "    segmentations.segmented_SeriesInstanceUID AS image_serieUID,\n",
        "    segmentations.StudyInstanceUID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.segmentations` AS segmentations\n",
        "  JOIN\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dicom_all\n",
        "  ON\n",
        "    segmentations.SeriesInstanceUID = dicom_all.SeriesInstanceUID\n",
        "  WHERE\n",
        "    segmentations.SegmentedPropertyType.CodeValue = '279706003'\n",
        "    AND dicom_all.collection_id = \"prostatex\"\n",
        "    AND segmentations.SegmentedPropertyType.CodingSchemeDesignator = 'SCT'\n",
        "    AND segmentations.SegmentAlgorithmType in UNNEST(['MANUAL'])\n",
        "    AND SeriesDescription NOT LIKE '%AIMI%'\n",
        "    AND Modality = 'SEG'),\n",
        "  idc_seg_whole_prostate AS(\n",
        "  SELECT\n",
        "    DISTINCT segmentations.SeriesInstanceUID,\n",
        "    segmentations.segmented_SeriesInstanceUID AS image_serieUID,\n",
        "    segmentations.StudyInstanceUID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.segmentations` AS segmentations\n",
        "  JOIN\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dicom_all\n",
        "  ON\n",
        "    segmentations.SeriesInstanceUID = dicom_all.SeriesInstanceUID\n",
        "  WHERE\n",
        "    segmentations.SegmentedPropertyType.CodeValue = '41216001'\n",
        "    AND dicom_all.collection_id = \"prostatex\"\n",
        "    AND segmentations.SegmentedPropertyType.CodingSchemeDesignator = 'SCT'\n",
        "    AND segmentations.SegmentAlgorithmType in UNNEST(['MANUAL'])\n",
        "    AND SeriesDescription NOT LIKE '%AIMI%'\n",
        "    AND Modality = 'SEG'),\n",
        "  t2_series AS(\n",
        "  SELECT\n",
        "    DISTINCT dc_all.SeriesInstanceUID,\n",
        "    dc_all.StudyInstanceUID,\n",
        "    dc_all.PatientID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_all\n",
        "  WHERE\n",
        "    dc_all.collection_id = 'prostatex'\n",
        "    AND dc_all.Modality = 'MR'\n",
        "    AND LOWER(dc_all.SeriesDescription) LIKE '%t2_tse_tra%'\n",
        "  ORDER BY\n",
        "    PatientID),\n",
        "  adc_series AS(\n",
        "  SELECT\n",
        "    dc_adc.StudyInstanceUID,\n",
        "    ARRAY_AGG(dc_adc.SeriesInstanceUID\n",
        "    ORDER BY\n",
        "      dc_adc.SeriesDate, dc_adc.SeriesTime DESC\n",
        "    LIMIT\n",
        "      1)[SAFE_OFFSET(0)] AS SeriesInstanceUID,\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_adc\n",
        "  JOIN\n",
        "    t2_series\n",
        "  ON\n",
        "    dc_adc.StudyInstanceUID = t2_series.StudyInstanceUID\n",
        "  WHERE\n",
        "    LOWER(dc_adc.SeriesDescription) LIKE '%adc%'\n",
        "    AND collection_id = \"prostatex\"\n",
        "  GROUP BY\n",
        "    dc_adc.StudyInstanceUID),\n",
        "  concat_t2_adc_series AS (\n",
        "  SELECT\n",
        "    t2_series.SeriesInstanceUID AS t2_serieUID,\n",
        "    adc_series.SeriesInstanceUID AS adc_serieUID,\n",
        "    t2_series.StudyInstanceUID\n",
        "  FROM\n",
        "    t2_series\n",
        "  INNER JOIN\n",
        "    adc_series\n",
        "  ON\n",
        "    t2_series.StudyInstanceUID = adc_series.StudyInstanceUID)\n",
        "SELECT\n",
        "  concat_t2_adc_series.t2_serieUID,\n",
        "  concat_t2_adc_series.adc_serieUID,\n",
        "  concat_t2_adc_series.StudyInstanceUID,\n",
        "  idc_zonal_prostate_seg.SeriesInstanceUID as expert_zonal_prostate_serieUID,\n",
        "  idc_whole_prostate_seg.SeriesInstanceUID as expert_whole_prostate_serieUID,\n",
        "  idc_zonal_prostate_seg.image_serieUID as expert_zonal_prostate_image_serieUID,\n",
        "  idc_whole_prostate_seg.image_serieUID as expert_whole_prostate_image_serieUID,\n",
        "  idc_zonal_prostate_seg.StudyInstanceUID as expert_zonal_prostate_studyUID,\n",
        "  idc_whole_prostate_seg.StudyInstanceUID as expert_whole_prostate_studyUID\n",
        "FROM\n",
        "  concat_t2_adc_series\n",
        "INNER JOIN idc_seg_zonal_prostate idc_zonal_prostate_seg ON concat_t2_adc_series.t2_serieUID = idc_zonal_prostate_seg.image_serieUID\n",
        "INNER JOIN idc_seg_whole_prostate idc_whole_prostate_seg ON concat_t2_adc_series.t2_serieUID = idc_whole_prostate_seg.image_serieUID"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "7d6de003e6d148d290c0b2f34f024ce5"
          ]
        },
        "outputId": "f27ac806-cd42-4316-ea03-ec8d5848459c",
        "id": "8mmeOOF6BiM_"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Query is running:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1b610a4c1504396b1a9dddb3bedfb4f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "2718505d9a2545bc82f7022b26fdffff"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's query all T2W images from ProstateX that have ONLY zonal SEG DCM objects -- n=32"
      ],
      "metadata": {
        "id": "n0kknF6OBz7l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery cohort_prostatex_zonal_only_df --project idc-sandbox-003\n",
        "WITH\n",
        "  idc_seg_zonal_prostate AS(\n",
        "  SELECT\n",
        "    DISTINCT segmentations.SeriesInstanceUID,\n",
        "    segmentations.segmented_SeriesInstanceUID AS image_serieUID,\n",
        "    segmentations.StudyInstanceUID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.segmentations` AS segmentations\n",
        "  JOIN\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dicom_all\n",
        "  ON\n",
        "    segmentations.SeriesInstanceUID = dicom_all.SeriesInstanceUID\n",
        "  WHERE\n",
        "    segmentations.SegmentedPropertyType.CodeValue = '279706003'\n",
        "    AND dicom_all.collection_id = \"prostatex\"\n",
        "    AND segmentations.SegmentedPropertyType.CodingSchemeDesignator = 'SCT'\n",
        "    AND segmentations.SegmentAlgorithmType in UNNEST(['MANUAL'])\n",
        "    AND SeriesDescription NOT LIKE '%AIMI%'\n",
        "    AND Modality = 'SEG'),\n",
        "  idc_seg_whole_prostate AS(\n",
        "  SELECT\n",
        "    DISTINCT segmentations.SeriesInstanceUID,\n",
        "    segmentations.segmented_SeriesInstanceUID AS image_serieUID,\n",
        "    segmentations.StudyInstanceUID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.segmentations` AS segmentations\n",
        "  JOIN\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dicom_all\n",
        "  ON\n",
        "    segmentations.SeriesInstanceUID = dicom_all.SeriesInstanceUID\n",
        "  WHERE\n",
        "    segmentations.SegmentedPropertyType.CodeValue = '41216001'\n",
        "    AND dicom_all.collection_id = \"prostatex\"\n",
        "    AND segmentations.SegmentedPropertyType.CodingSchemeDesignator = 'SCT'\n",
        "    AND segmentations.SegmentAlgorithmType in UNNEST(['MANUAL'])\n",
        "    AND SeriesDescription NOT LIKE '%AIMI%'\n",
        "    AND Modality = 'SEG'),\n",
        "  t2_series AS(\n",
        "  SELECT\n",
        "    DISTINCT dc_all.SeriesInstanceUID,\n",
        "    dc_all.StudyInstanceUID,\n",
        "    dc_all.PatientID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_all\n",
        "  WHERE\n",
        "    dc_all.collection_id = 'prostatex'\n",
        "    AND dc_all.Modality = 'MR'\n",
        "    AND LOWER(dc_all.SeriesDescription) LIKE '%t2_tse_tra%'\n",
        "  ORDER BY\n",
        "    PatientID),\n",
        "  adc_series AS(\n",
        "  SELECT\n",
        "    dc_adc.StudyInstanceUID,\n",
        "    ARRAY_AGG(dc_adc.SeriesInstanceUID\n",
        "    ORDER BY\n",
        "      dc_adc.SeriesDate, dc_adc.SeriesTime DESC\n",
        "    LIMIT\n",
        "      1)[SAFE_OFFSET(0)] AS SeriesInstanceUID,\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_adc\n",
        "  JOIN\n",
        "    t2_series\n",
        "  ON\n",
        "    dc_adc.StudyInstanceUID = t2_series.StudyInstanceUID\n",
        "  WHERE\n",
        "    LOWER(dc_adc.SeriesDescription) LIKE '%adc%'\n",
        "    AND collection_id = \"prostatex\"\n",
        "  GROUP BY\n",
        "    dc_adc.StudyInstanceUID),\n",
        "  concat_t2_adc_series AS (\n",
        "  SELECT\n",
        "    t2_series.SeriesInstanceUID AS t2_serieUID,\n",
        "    adc_series.SeriesInstanceUID AS adc_serieUID,\n",
        "    t2_series.StudyInstanceUID\n",
        "  FROM\n",
        "    t2_series\n",
        "  INNER JOIN\n",
        "    adc_series\n",
        "  ON\n",
        "    t2_series.StudyInstanceUID = adc_series.StudyInstanceUID)\n",
        "SELECT\n",
        "  concat_t2_adc_series.t2_serieUID,\n",
        "  concat_t2_adc_series.adc_serieUID,\n",
        "  concat_t2_adc_series.StudyInstanceUID,\n",
        "  idc_zonal_prostate_seg.SeriesInstanceUID as expert_zonal_prostate_serieUID,\n",
        "  idc_whole_prostate_seg.SeriesInstanceUID as expert_whole_prostate_serieUID,\n",
        "  idc_zonal_prostate_seg.image_serieUID as expert_zonal_prostate_image_serieUID,\n",
        "  idc_whole_prostate_seg.image_serieUID as expert_whole_prostate_image_serieUID,\n",
        "  idc_zonal_prostate_seg.StudyInstanceUID as expert_zonal_prostate_studyUID,\n",
        "  idc_whole_prostate_seg.StudyInstanceUID as expert_whole_prostate_studyUID\n",
        "FROM\n",
        "  concat_t2_adc_series\n",
        "INNER JOIN idc_seg_zonal_prostate idc_zonal_prostate_seg ON concat_t2_adc_series.t2_serieUID = idc_zonal_prostate_seg.image_serieUID\n",
        "LEFT JOIN idc_seg_whole_prostate idc_whole_prostate_seg ON concat_t2_adc_series.t2_serieUID = idc_whole_prostate_seg.image_serieUID\n",
        "WHERE idc_whole_prostate_seg.image_serieUID is NULL"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "outputId": "6afa77e2-6e71-410e-88d7-07385e754a26",
        "id": "tjfjn9yUBz7s"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Query is running:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ccb8e6646dd4da9ac9313f9750faea0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ecff16accc604494973566b531fdfdb3"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## QIN-Prostate-Repeatability collection"
      ],
      "metadata": {
        "id": "cxoJmbDZ9ioV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery cohort_qin_prostate_repeatability_df --project idc-sandbox-003\n",
        "WITH\n",
        "  # we know there is only one prostate segmentation per study\n",
        "  idc_seg_whole_prostate AS (\n",
        "  SELECT\n",
        "    segmentations.StudyInstanceUID,\n",
        "    segmentations.SeriesInstanceUID,\n",
        "    segmentations.segmented_SeriesInstanceUID,\n",
        "    # for debugging:\n",
        "    dicom_all.SeriesDescription\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.segmentations` AS segmentations\n",
        "  JOIN\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dicom_all\n",
        "  ON\n",
        "    segmentations.SeriesInstanceUID = dicom_all.SeriesInstanceUID\n",
        "  WHERE\n",
        "    dicom_all.SeriesDescription = \"T2 Weighted Axial Segmentations\"\n",
        "    AND segmentations.SegmentedPropertyType.CodeValue = 'T-9200B'\n",
        "    AND segmentations.SegmentAlgorithmType in UNNEST(['MANUAL'])\n",
        "    AND segmentations.SegmentedPropertyType.CodingSchemeDesignator = 'SRT'),\n",
        "  # select T2, here we do not check for multiple T2 within the same study, since we know there is only one per study\n",
        "  t2_series AS(\n",
        "  SELECT\n",
        "    DISTINCT dc_all.SeriesInstanceUID,\n",
        "    dc_all.StudyInstanceUID,\n",
        "    dc_all.PatientID\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_all\n",
        "  WHERE\n",
        "    dc_all.collection_id = 'qin_prostate_repeatability'\n",
        "    AND dc_all.Modality = 'MR'\n",
        "    AND LOWER(dc_all.SeriesDescription) LIKE '%t2%'\n",
        "  ORDER BY\n",
        "    PatientID),\n",
        "  # ADC selection\n",
        "  adc_series AS(\n",
        "  SELECT\n",
        "    dc_adc.StudyInstanceUID,\n",
        "    ARRAY_AGG(dc_adc.SeriesInstanceUID\n",
        "    ORDER BY\n",
        "      dc_adc.SeriesDate, dc_adc.SeriesTime DESC\n",
        "    LIMIT\n",
        "      1)[SAFE_OFFSET(0)] AS SeriesInstanceUID,\n",
        "  FROM\n",
        "    `bigquery-public-data.idc_current.dicom_all` AS dc_adc\n",
        "  JOIN\n",
        "    t2_series\n",
        "  ON\n",
        "    dc_adc.StudyInstanceUID = t2_series.StudyInstanceUID\n",
        "  WHERE\n",
        "    LOWER(dc_adc.SeriesDescription) LIKE '%apparent%'\n",
        "  GROUP BY\n",
        "    dc_adc.StudyInstanceUID)\n",
        "SELECT\n",
        "  t2_series.SeriesInstanceUID AS t2_serieUID,\n",
        "  adc_series.SeriesInstanceUID AS adc_serieUID,\n",
        "  idc_seg_whole_prostate.SeriesInstanceUID AS expertWholeProstateSeriesInstanceUID,\n",
        "  idc_seg_whole_prostate.SeriesDescription AS expertWholeProstateSeriesDescription,\n",
        "  t2_series.StudyInstanceUID\n",
        "FROM\n",
        "  t2_series\n",
        "INNER JOIN\n",
        "  adc_series\n",
        "ON\n",
        "  t2_series.StudyInstanceUID = adc_series.StudyInstanceUID\n",
        "INNER JOIN\n",
        "  idc_seg_whole_prostate\n",
        "ON\n",
        "  t2_series.SeriesInstanceUID = idc_seg_whole_prostate.segmented_SeriesInstanceUID"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "id": "HynX2U9gD3zc",
        "outputId": "78d2c24b-e45c-46c7-b1cb-3b95f918a9f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Query is running:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3784060b15c5457b97c999065619af3f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6961cd76e434f3da2fa8d0e4ae320ff"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generate .tsv terra data tables per collection basis"
      ],
      "metadata": {
        "id": "m5h1qydP9nAi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prostate-MRI-US-Biopsy"
      ],
      "metadata": {
        "id": "R8srwRL69sNY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# asdf\n",
        "terra_prostate_mri_us_biopsy_df = build_data_table(idc_data_df=cohort_prostate_mri_us_biopsy_df,\n",
        "  terra_col_scheme_lst=terra_data_columns,\n",
        "  terra_table_id=terra_table_id_prostate_mri_us_biopsy,\n",
        "  terra_col_values_lst=terra_prostate_mri_us_biopsy_values,\n",
        "  idcSegColName=\"expertWholeProstateSeriesInstanceUID\",\n",
        "  t2ImageColName=\"t2_serieUID\",\n",
        "  adcImageColName=\"adc_serieUID\",\n",
        "  idcSegColNameAdd=None)"
      ],
      "metadata": {
        "id": "er0zsu8uTPZd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_prostate_mri_us_biopsy_df.to_csv(\"prostate_mri_us_biopsy.tsv\", sep=\"\\t\", index=None)"
      ],
      "metadata": {
        "id": "PXeTPGKvT0q7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ProstateX"
      ],
      "metadata": {
        "id": "wk-p7DCS9xD5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_prostatex_zonal_whole_df = build_data_table(idc_data_df=cohort_prostatex_zonal_whole_df,\n",
        "  terra_col_scheme_lst=terra_data_columns,\n",
        "  terra_table_id=terra_table_id_prostatex_zonal_whole,\n",
        "  terra_col_values_lst=terra_prostatex_zonal_whole_values,\n",
        "  idcSegColName=\"expert_whole_prostate_serieUID\",\n",
        "  t2ImageColName=\"t2_serieUID\",\n",
        "  adcImageColName=\"adc_serieUID\",\n",
        "  idcSegColNameAdd=\"expert_zonal_prostate_serieUID\")"
      ],
      "metadata": {
        "id": "58i2oHuBVZpu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_prostatex_zonal_only_df = build_data_table(idc_data_df=cohort_prostatex_zonal_only_df,\n",
        "  terra_col_scheme_lst=terra_data_columns,\n",
        "  terra_table_id=terra_table_id_prostatex_zonal_only,\n",
        "  terra_col_values_lst=terra_prostatex_zonal_only_values,\n",
        "  idcSegColName=\"expert_zonal_prostate_serieUID\",\n",
        "  t2ImageColName=\"t2_serieUID\",\n",
        "  adcImageColName=\"adc_serieUID\",\n",
        "  idcSegColNameAdd=None)"
      ],
      "metadata": {
        "id": "VvIjT8VbX6uG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Combine both dfs together"
      ],
      "metadata": {
        "id": "Wmn9mvpsYh9p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_prostatex_all_df = pd.concat([terra_prostatex_zonal_whole_df.drop(columns=[f\"entity:{terra_table_id_prostatex_zonal_whole}_id\"], axis=1).reset_index(drop=True),\n",
        "                                 terra_prostatex_zonal_only_df.drop(columns=[f\"entity:{terra_table_id_prostatex_zonal_only}_id\"], axis=1).reset_index(drop=True)], ignore_index=True)"
      ],
      "metadata": {
        "id": "Jg1OxDyvYn6E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_prostatex_all_df[f\"entity:terra_mhub_prostatex_all_test_id\"] = [x for x in range(0,len(terra_prostatex_all_df))]\n",
        "df_cols = list(terra_prostatex_all_df.columns.values)\n",
        "cols = [df_cols[-1]] + df_cols[:-1]\n",
        "terra_prostatex_all_df = terra_prostatex_all_df[cols]"
      ],
      "metadata": {
        "id": "Mz7F-d6FYkVr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_prostatex_all_df.to_csv(\"prostatex_all.tsv\", sep=\"\\t\", index=None)"
      ],
      "metadata": {
        "id": "6IwOlNrxdGku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## QIN-Prostate-Repeatability"
      ],
      "metadata": {
        "id": "R1Xc7ekP9zS7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_qin_prostate_repeatability_df = build_data_table(idc_data_df=cohort_qin_prostate_repeatability_df,\n",
        "  terra_col_scheme_lst=terra_data_columns,\n",
        "  terra_table_id=terra_table_id_qin_prostate_repeatability,\n",
        "  terra_col_values_lst=terra_qin_prostate_repeatability_values,\n",
        "  t2ImageColName=\"t2_serieUID\",\n",
        "  adcImageColName=\"adc_serieUID\",\n",
        "  idcSegColName=\"expertWholeProstateSeriesInstanceUID\",\n",
        "  idcSegColNameAdd=None)"
      ],
      "metadata": {
        "id": "fB2QVFOMYhGL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_qin_prostate_repeatability_df.to_csv(\"qin_prostate_repeatability.tsv\", sep=\"\\t\", index=None)"
      ],
      "metadata": {
        "id": "YnVnyF5tduD1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Combine all the terra data together"
      ],
      "metadata": {
        "id": "9zYJf1uteOgc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terra_all_df = pd.concat([terra_prostate_mri_us_biopsy_df.drop(columns=[f\"entity:{terra_table_id_prostate_mri_us_biopsy}_id\"],\n",
        "                                                           axis=1).reset_index(drop=True),\n",
        "                                 terra_prostatex_all_df.drop(columns=[f\"entity:terra_mhub_prostatex_all_test_id\"],\n",
        "                                                                    axis=1).reset_index(drop=True),\n",
        "                                 terra_qin_prostate_repeatability_df.drop(columns=[f\"entity:{terra_table_id_qin_prostate_repeatability}_id\"],\n",
        "                                                                    axis=1).reset_index(drop=True)], ignore_index=True)\n"
      ],
      "metadata": {
        "id": "w01eysNIeSne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_all_df[f\"entity:terra_mhub_all_collections_v3_id\"] = [int(x) for x in range(0,len(terra_all_df))]\n",
        "terra_all_df = terra_all_df.reset_index(drop=True)\n",
        "df_cols = list(terra_all_df.columns.values)\n",
        "cols = [df_cols[-1]] + df_cols[:-1]\n",
        "terra_all_df = terra_all_df[cols]"
      ],
      "metadata": {
        "id": "HtpDVyC2e23U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "terra_all_df.to_csv(\"mhub_terra_all.tsv\", sep=\"\\t\", index=None)"
      ],
      "metadata": {
        "id": "8z7-ZiSQe23U"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}