From 0ef86cd65abe616d4264068482c4074a5441b098 Mon Sep 17 00:00:00 2001
From: vkt1414 <vthiriveedhi@mgh.harvard.edu>
Date: Mon, 10 Jun 2024 17:52:20 -0400
Subject: [PATCH] add files to extract radiomics features from json files

---
 .dockstore.yml                                |   8 +-
 .../convert_radiomics_features_json_to_df.yml |  63 ++
 .../Dockerfile                                |  25 +
 ...stProcessingRadiomicsJsonToDataFrame.ipynb | 246 +++++++
 ...ccessing_of_RadiomicsJsonToDataFrame.ipynb | 626 ++++++++++++++++++
 .../uploadJsonRadiomicsToBigquery.ipynb       | 171 +++++
 .../convertRadiomicsJsonToDataframe.wdl       |  91 +++
 7 files changed, 1229 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/convert_radiomics_features_json_to_df.yml
 create mode 100644 workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_df/Dockerfile
 create mode 100644 workflows/TotalSegmentator/Notebooks/postProcessingRadiomicsJsonToDataFrame.ipynb
 create mode 100644 workflows/TotalSegmentator/Notebooks/preProccessing_of_RadiomicsJsonToDataFrame.ipynb
 create mode 100644 workflows/TotalSegmentator/Notebooks/uploadJsonRadiomicsToBigquery.ipynb
 create mode 100644 workflows/TotalSegmentator/Terra/postProcessing/convertRadiomicsJsonToDataframe.wdl

diff --git a/.dockstore.yml b/.dockstore.yml
index 516214d..dc313f7 100644
--- a/.dockstore.yml
+++ b/.dockstore.yml
@@ -23,7 +23,13 @@ workflows:
      primaryDescriptorPath: /workflows/TotalSegmentator/Terra/postProcessing/perFrame.wdl   
      authors:
       - name: ImagingDataCommons
-        email: vthiriveedhi@mgb.org        
+        email: vthiriveedhi@mgb.org
+   - name: convertRadiomicsJsonToDataframeOnTerra
+     subclass: WDL
+     primaryDescriptorPath: /workflows/TotalSegmentator/Terra/postProcessing/convertRadiomicsJsonToDataframe.wdl   
+     authors:
+      - name: ImagingDataCommons
+        email: vthiriveedhi@mgb.org           
    - name: TotalSegmentatortwoVmWorkflowOnSB-CGC
      subclass: CWL
      primaryDescriptorPath: /workflows/TotalSegmentator/SevenBridges/splitWorkflow/twoVM.cwl
diff --git a/.github/workflows/convert_radiomics_features_json_to_df.yml b/.github/workflows/convert_radiomics_features_json_to_df.yml
new file mode 100644
index 0000000..f95d78b
--- /dev/null
+++ b/.github/workflows/convert_radiomics_features_json_to_df.yml
@@ -0,0 +1,63 @@
+name: convert-radiomics-features-json-dataframe
+
+on:
+  push:
+    branches: 
+      - "main" 
+    paths:
+      - ".github/workflows/convert_radiomics_features_json_to_df.yml"
+      - "workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_df/Dockerfile"
+    tags:
+      - 'v*'   
+  pull_request:
+    branches:
+      - "main" 
+    paths:
+      - ".github/workflows/convert_radiomics_features_json_to_df.yml"
+      - "workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_df/Dockerfile"
+  workflow_dispatch:  
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Git
+        run: |
+          git config --global user.email "actions@github.com"
+          git config --global user.name "GitHub Actions"
+
+      - name: Get Git Commit Hash
+        id: git-commit-hash
+        run: |
+          COMMIT_HASH=$(git rev-parse HEAD)
+          echo "COMMIT_HASH=$COMMIT_HASH" >> $GITHUB_ENV
+
+      - name: Docker meta
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: |
+           imagingdatacommons/convert_radiomics_features_json_to_df
+
+      - name: Login to Docker Hub
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Build and Push Docker image to Docker Hub 
+        if:           
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: ./workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_df/Dockerfile
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: ${{ steps.meta.outputs.tags }}
+          build-args: |
+            GIT_HASH=${{ env.COMMIT_HASH }}
+        env:
+          COMMIT_HASH: ${{ env.COMMIT_HASH }}      
diff --git a/workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_df/Dockerfile b/workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_df/Dockerfile
new file mode 100644
index 0000000..ee03419
--- /dev/null
+++ b/workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_df/Dockerfile
@@ -0,0 +1,25 @@
+FROM python@sha256:5b287042a6150052420e6a7fb7c1606b6403740880897ae9610faf434da28693
+
+ARG GIT_HASH
+
+LABEL BASE_DOCKER_IMAGE="python:3.11.2-slim-buster"\
+      MAINTAINER="IDC <vthiriveedhi@mgb.org>" \
+      GIT_HASH=${GIT_HASH}\
+      PATH_TO_DOCKER_FILE="https://github.com/imagingdatacommons/CloudSegmentator/blob/${GIT_HASH}/workflows/TotalSegmentator/Dockerfiles/convert_radiomics_features_json_to_dataframe/Dockerfile"\
+      IMAGE_NAME_ON_DOCKERHUB="imagingdatacommons/convert_radiomics_features_json_to_dataframe"
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    lz4\
+    curl\
+    wget\
+    && rm -rf /var/lib/apt/lists/*
+    
+RUN pip3 install --no-cache-dir\   
+    ipykernel==6.22.0\    
+    ipython==8.11.0\    
+    ipywidgets==8.0.5\    
+    jupyter==1.0.0\     
+    pandas==1.5.3\    
+    papermill==2.4.0\
+    pyarrow==16.1.0
diff --git a/workflows/TotalSegmentator/Notebooks/postProcessingRadiomicsJsonToDataFrame.ipynb b/workflows/TotalSegmentator/Notebooks/postProcessingRadiomicsJsonToDataFrame.ipynb
new file mode 100644
index 0000000..540bb58
--- /dev/null
+++ b/workflows/TotalSegmentator/Notebooks/postProcessingRadiomicsJsonToDataFrame.ipynb
@@ -0,0 +1,246 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "3e33103f",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/vkt1414/CloudSegmentator/blob/feat-convert-raw-radiomics-to-dataframe/workflows/TotalSegmentator/Notebooks/postProcessingRadiomicsJsonToDataFrame.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "kdOyoySwehlv",
+      "metadata": {
+        "id": "kdOyoySwehlv"
+      },
+      "source": [
+        "## **This notebook converts raw radiomics features in JSON format to a pandas dataframe. It takes the raw radiomics files in lz4 format as input, decompresses them, and flattens them to a dataframe, output a csv.lz4**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "-LdyuHrrPigD",
+      "metadata": {
+        "id": "-LdyuHrrPigD"
+      },
+      "source": [
+        "### **Installing Packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "B5KuCCFhOgpA",
+      "metadata": {
+        "id": "B5KuCCFhOgpA"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "    !sudo apt-get update \\\n",
+        "    && apt-get install -y --no-install-recommends \\\n",
+        "    lz4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "73LFRISZPn6Q",
+      "metadata": {
+        "id": "73LFRISZPn6Q"
+      },
+      "source": [
+        "### **Importing Packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "db4585bb-9bfb-4de3-882a-c7454c7ec3af",
+      "metadata": {
+        "id": "db4585bb-9bfb-4de3-882a-c7454c7ec3af"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import sys\n",
+        "import subprocess\n",
+        "import json\n",
+        "import pandas as pd\n",
+        "from pandas import json_normalize"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Clrt8j70Pb5a",
+      "metadata": {
+        "id": "Clrt8j70Pb5a"
+      },
+      "source": [
+        "### **Parameters for papermill**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "4527d881",
+      "metadata": {
+        "id": "4527d881",
+        "tags": [
+          "parameters"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "if 'google.colab' in sys.modules:\n",
+        "    !wget -q https://github.com/vkt1414/CloudSegmentator/releases/download/test/pyradiomicsRadiomicsFeatures.tar.lz4\n",
+        "    rawJsonRadiomicsFiles=[\"pyradiomicsRadiomicsFeatures.tar.lz4\"]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "efd80612",
+      "metadata": {
+        "id": "efd80612"
+      },
+      "source": [
+        "### **This is the cell used on cloud, as the file paths are passed to the notebook as a string**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "557de167",
+      "metadata": {
+        "id": "557de167"
+      },
+      "outputs": [],
+      "source": [
+        "if not 'google.colab' in sys.modules:\n",
+        "    rawJsonRadiomicsFiles=rawJsonRadiomicsFiles.split(',')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "ae662d02",
+      "metadata": {
+        "id": "ae662d02"
+      },
+      "outputs": [],
+      "source": [
+        "def flatten_json(seriesInstanceUID, radiomics_file_path):\n",
+        "    try:\n",
+        "        with open(radiomics_file_path, 'r', encoding='utf-8', errors='ignore') as f:\n",
+        "            data = json.load(f, strict=False)\n",
+        "    except json.JSONDecodeError as e:\n",
+        "        print(f\"Error decoding JSON: {e}\")\n",
+        "        # Write the file path to the error log\n",
+        "        with open('error_file.txt', 'a') as error_file:\n",
+        "            error_file.write(f\"{radiomics_file_path}\\n\")\n",
+        "        return None \n",
+        "\n",
+        "    # Create an empty list to store DataFrames\n",
+        "    df_list = []\n",
+        "\n",
+        "    # Iterate over the items in the dictionary and flatten each to a row\n",
+        "    for organ, properties in data.items():\n",
+        "        # Normalize the nested dictionary\n",
+        "        organ_df = json_normalize(properties)\n",
+        "        # Add SeriesInstanceUID\n",
+        "        organ_df['seriesInstanceUID'] = seriesInstanceUID\n",
+        "        # Add the organ name as a column\n",
+        "        organ_df['organ'] = organ\n",
+        "        # Append the result to the list\n",
+        "        df_list.append(organ_df)\n",
+        "\n",
+        "    # Concatenate all DataFrames in the list\n",
+        "    df = pd.concat(df_list, ignore_index=True)\n",
+        "\n",
+        "    return df\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "nbzivnkRnb_B",
+      "metadata": {
+        "id": "nbzivnkRnb_B"
+      },
+      "source": [
+        "### **Convert Radiomics features in JSON to DataFrame, finally to a csv**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ZHSbg5CrWX1Q",
+      "metadata": {
+        "id": "ZHSbg5CrWX1Q"
+      },
+      "outputs": [],
+      "source": [
+        "# Main script to decompress files and flatten JSON\n",
+        "all_dataframes = []  # List to store all DataFrames\n",
+        "\n",
+        "for rawJsonRadiomicsFile in rawJsonRadiomicsFiles:\n",
+        "  # Decompress the LZ4 file and extract the tar file\n",
+        "  !lz4 -d --rm $rawJsonRadiomicsFile -c | tar  -xvf -\n",
+        "  # Assuming 'radiomics' is a directory in the current working directory\n",
+        "  for series_folder in os.listdir('radiomics'):\n",
+        "    # The directory name is the seriesInstanceUID\n",
+        "    seriesInstanceUID = series_folder\n",
+        "    for file in os.listdir(os.path.join('radiomics',series_folder)):\n",
+        "        if file.endswith('_raw.json.lz4'):\n",
+        "            # Construct the full file path\n",
+        "            file_path = os.path.join('radiomics',series_folder, file)\n",
+        "            # Decompress the file using the lz4 command\n",
+        "            !lz4 -d --rm $file_path\n",
+        "            # Truncate to get the JSON filename\n",
+        "            json_file_path=file_path[:-4]\n",
+        "            # Flatten the JSON file into a DataFrame\n",
+        "            df = flatten_json(seriesInstanceUID, json_file_path)\n",
+        "            # Add the DataFrame to the list\n",
+        "            all_dataframes.append(df)\n",
+        "  !rm -r radiomics\n",
+        "\n",
+        "# Concatenate all DataFrames in the list\n",
+        "final_df = pd.concat(all_dataframes, ignore_index=True)\n",
+        "\n",
+        "# Save the final DataFrame to a CSV file\n",
+        "final_df.to_parquet('raw_radiomics.parquet', index=False)\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "celltoolbar": "Tags",
+    "colab": {
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/workflows/TotalSegmentator/Notebooks/preProccessing_of_RadiomicsJsonToDataFrame.ipynb b/workflows/TotalSegmentator/Notebooks/preProccessing_of_RadiomicsJsonToDataFrame.ipynb
new file mode 100644
index 0000000..743dc62
--- /dev/null
+++ b/workflows/TotalSegmentator/Notebooks/preProccessing_of_RadiomicsJsonToDataFrame.ipynb
@@ -0,0 +1,626 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/vkt1414/CloudSegmentator/blob/feat-convert-raw-radiomics-to-dataframe/workflows/TotalSegmentator/Notebooks/preProccessing_of_RadiomicsJsonToDataFrame.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "qWeL6RS4pVtu",
+      "metadata": {
+        "id": "qWeL6RS4pVtu"
+      },
+      "source": [
+        "## **This notebook provides a step-by-step guide on how to generate a datatable for Terra. This datatable is essential for converting the radiomics features in json format to a pandas dataframe, using the workflow linked below.**\n",
+        "\n",
+        "You can find the PerFrameFunctionalGroupsSequence Extraction workflow [here](https://dockstore.org/workflows/github.com/ImagingDataCommons/CloudSegmentator/convert_radiomics_features_json_to_dataframe:main?tab=info).\n",
+        "\n",
+        "The workflow requires list of paths to the lz4 compressed DICOM SEG objects generated by our TotalSegmentator workflows like [here](https://github.com/ImagingDataCommons/CloudSegmentator/blob/main/workflows/TotalSegmentator/Notebooks/dicomsegAndRadiomicsSR_Notebook.ipynb). While running the workflow on Cloud, each VM is assigned 100 (chosen arbitarily can be any number) batches of compressed json files containing raw radiomics features, amounting to up to 1200 json files per VM.\n",
+        "\n",
+        "Once these steps are completed, a datatable is produced and is ready to be uploaded to Terra's data tables"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "-LdyuHrrPigD",
+      "metadata": {
+        "id": "-LdyuHrrPigD"
+      },
+      "source": [
+        "### **Installing Packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "B5KuCCFhOgpA",
+      "metadata": {
+        "id": "B5KuCCFhOgpA"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "!sudo apt-get update \\\n",
+        "  && apt-get install -y --no-install-recommends \\\n",
+        "  lz4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "73LFRISZPn6Q",
+      "metadata": {
+        "id": "73LFRISZPn6Q"
+      },
+      "source": [
+        "### **Importing Packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "db4585bb-9bfb-4de3-882a-c7454c7ec3af",
+      "metadata": {
+        "id": "db4585bb-9bfb-4de3-882a-c7454c7ec3af"
+      },
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "import json\n",
+        "import math\n",
+        "import os\n",
+        "import pandas as pd\n",
+        "import subprocess\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1mqxvpePhxA",
+      "metadata": {
+        "id": "d1mqxvpePhxA"
+      },
+      "source": [
+        "### **Example Terra datatable**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "Bri49R5rsPsW",
+      "metadata": {
+        "id": "Bri49R5rsPsW"
+      },
+      "outputs": [],
+      "source": [
+        "segFilesCsv='https://github.com/ImagingDataCommons/CloudSegmentator/releases/download/v1.0.0/sample_manifest_for_perframe.tsv'\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fU_QT-Dnsakj",
+      "metadata": {
+        "id": "fU_QT-Dnsakj"
+      },
+      "source": [
+        "### **Read the tsv from twoVMworkflow datatable on terra**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "Kgt7OeFzP1BX",
+      "metadata": {
+        "id": "Kgt7OeFzP1BX"
+      },
+      "outputs": [],
+      "source": [
+        "data= pd.read_table(segFilesCsv)\n",
+        "data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "de-zRHR4InP8",
+      "metadata": {
+        "id": "de-zRHR4InP8"
+      },
+      "source": [
+        "### **Generate manifests for Terra datatable**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "N0dTxO-P6TQx",
+      "metadata": {
+        "id": "N0dTxO-P6TQx",
+        "outputId": "f2462bef-3a9d-4fbf-be0e-c8c5b85ec447",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    entity:rawRadiomics_2024_05_15_20_18_id  \\\n",
+              "0                                         1   \n",
+              "1                                         2   \n",
+              "2                                         3   \n",
+              "3                                         4   \n",
+              "4                                         5   \n",
+              "..                                      ...   \n",
+              "101                                     102   \n",
+              "102                                     103   \n",
+              "103                                     104   \n",
+              "104                                     105   \n",
+              "105                                     106   \n",
+              "\n",
+              "                          pyradiomicsRadiomicsFeatures  \n",
+              "0    [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "1    [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "2    [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "3    [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "4    [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "..                                                 ...  \n",
+              "101  [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "102  [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "103  [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "104  [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "105  [\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642...  \n",
+              "\n",
+              "[106 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
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\\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/b2e19481-271d-48f5-b976-3889f3cb673b/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/862b91b4-ca9e-4f3e-9b27-d56ec298c485/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/174cef33-790e-4781-9c56-b4a2163c6e80/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/10a152f4-9419-4241-ae32-1408780270fa/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/13a702cd-7ce4-4094-8426-76e5adb5f5c3/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/34ca8ae2-3c2f-4b76-a382-6cfcd1e54be0/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/d1212f46-3743-4e5e-b949-5ed039743340/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/19f20dda-e844-4f86-b5a0-bef33de0d4d2/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/4c300af9-2ac5-490f-8017-659f60f62aaf/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/1aa8140a-f0d8-4af5-a300-5831d044d1d9/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\", \\\"gs://fc-5af492dc-6993-4c91-bbf6-3e2747868642/submissions/f835e545-da03-4a6f-89b6-6460a9497ac1/TotalSegmentator/a75ac568-c46e-4c31-8a86-b7916a018ad5/call-dicomsegAndRadiomicsSR/pyradiomicsRadiomicsFeatures.tar.lz4\\\"]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "# Set the number of rows per file\n",
+        "batches_per_row = 100\n",
+        "\n",
+        "# Sort the dataframe by the first non-index column (assuming it's 'batch_id')\n",
+        "df = data.sort_values(by=data.columns[0])\n",
+        "\n",
+        "# Calculate the number of files needed\n",
+        "num_files = math.ceil(len(df) / batches_per_row)\n",
+        "\n",
+        "# Split the dataframe into multiple dataframes\n",
+        "dfs = [df[i*batches_per_row:(i+1)*batches_per_row] for i in range(num_files)]\n",
+        "\n",
+        "# Get the current date and time formatted with underscores up to minutes\n",
+        "now = datetime.now().strftime('%Y_%m_%d_%H_%M')\n",
+        "\n",
+        "# Set the directory for the manifests\n",
+        "manifests_dir = 'manifests'\n",
+        "\n",
+        "# Make sure the directory exists\n",
+        "os.makedirs(manifests_dir, exist_ok=True)\n",
+        "\n",
+        "# Create a new column name for the batch_id column\n",
+        "batch_id_column = f'entity:rawRadiomics_{now}_id'\n",
+        "\n",
+        "# Create a new dataframe to store the batch information\n",
+        "batch_df = pd.DataFrame(columns=[batch_id_column, 'pyradiomicsRadiomicsFeatures'])\n",
+        "\n",
+        "# Analyze each file and add a row to the batch dataframe\n",
+        "for i, df_batch in enumerate(dfs):\n",
+        "    # Create a list of segFiles for this batch\n",
+        "    rawRadiomics_list = df_batch['pyradiomicsRadiomicsFeatures'].tolist()\n",
+        "\n",
+        "    # Convert the list to a JSON string with double quotes\n",
+        "    rawRadiomics_json = json.dumps(rawRadiomics_list)\n",
+        "\n",
+        "    # Create a new row with the batch information and the segFiles list\n",
+        "    new_row = pd.DataFrame({\n",
+        "        batch_id_column: [i+1],\n",
+        "        'pyradiomicsRadiomicsFeatures': [rawRadiomics_json],\n",
+        "    })\n",
+        "    # Add the new row to the batch dataframe\n",
+        "    batch_df = pd.concat([batch_df, new_row], ignore_index=True)\n",
+        "\n",
+        "batch_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "xq6iyJqf1guv",
+      "metadata": {
+        "id": "xq6iyJqf1guv"
+      },
+      "outputs": [],
+      "source": [
+        "batch_df.to_csv(f'rawRadiomics_{now}.tsv',sep=\"\\t\", index=False)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/workflows/TotalSegmentator/Notebooks/uploadJsonRadiomicsToBigquery.ipynb b/workflows/TotalSegmentator/Notebooks/uploadJsonRadiomicsToBigquery.ipynb
new file mode 100644
index 0000000..ebb3f6f
--- /dev/null
+++ b/workflows/TotalSegmentator/Notebooks/uploadJsonRadiomicsToBigquery.ipynb
@@ -0,0 +1,171 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/ImagingDataCommons/CloudSegmentator/blob/main/workflows/TotalSegmentator/Notebooks/uploadPerFrameToBigquery.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ViG4TuwcEIV"
+      },
+      "source": [
+        "## **This Notebook decompresses the csv.lz4 files containing general, shape and first order radiomics features and pushes the data to biqquery table**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DKUDHW9ycelj"
+      },
+      "source": [
+        "### **Installing Packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "laT39NA4kS3t"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "!apt-get install lz4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b-phJIbhkTOe"
+      },
+      "source": [
+        "### **Importing Packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iHFnDNE2bYdf"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import auth\n",
+        "from google.cloud import bigquery\n",
+        "import ast\n",
+        "import pandas as pd\n",
+        "auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7fjoeOL5f6DF"
+      },
+      "outputs": [],
+      "source": [
+        "project_id='my project'\n",
+        "client = bigquery.Client(project=project_id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v4nHyE_qfMKF"
+      },
+      "source": [
+        "### **Read the Terra data table with links to output files**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lAx35lYuf02s"
+      },
+      "outputs": [],
+      "source": [
+        "radiomics_terra_data_table=pd.read_table('path to tsv file with output links')\n",
+        "radiomics_terra_data_table"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D200phMaeCi8"
+      },
+      "source": [
+        "### **Decompress the csv.lz4, Read csv and load data to bigquery**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hcYZf1Y_h8ut"
+      },
+      "outputs": [],
+      "source": [
+        "parquet_files= radiomics_terra_data_table.parquet_radiomicsJsonToDataFrame.to_list()\n",
+        "parquet_files"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6dJKZV40i82t"
+      },
+      "source": [
+        "### **Choose a bigquery table**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zgTDbu5Ri8gO"
+      },
+      "outputs": [],
+      "source": [
+        "bq_table_name=\"project.dataset.table\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LeuOrqUHiGZM"
+      },
+      "outputs": [],
+      "source": [
+        "for parquet_file in parquet_files:\n",
+        "  !rm raw_radiomics.parquet\n",
+        "  !gsutil cp $parquet_file .\n",
+        "  json_radiomics_df= pd.read_parquet(parquet_file)\n",
+        "  json_radiomics_df.columns = json_radiomics_df.columns.str.replace('.', '_')\n",
+        "  # Now load the dataframe into BigQuery\n",
+        "  client.load_table_from_dataframe(json_radiomics_df, bq_table_name, job_config=bigquery.LoadJobConfig(autodetect=True))\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/workflows/TotalSegmentator/Terra/postProcessing/convertRadiomicsJsonToDataframe.wdl b/workflows/TotalSegmentator/Terra/postProcessing/convertRadiomicsJsonToDataframe.wdl
new file mode 100644
index 0000000..db0a06f
--- /dev/null
+++ b/workflows/TotalSegmentator/Terra/postProcessing/convertRadiomicsJsonToDataframe.wdl
@@ -0,0 +1,91 @@
+version 1.0
+#WORKFLOW DEFINITION
+workflow radiomicsJsonConversion {
+ input {
+   #all the inputs entered here but not hardcoded will appear in the UI as required fields
+   #And the hardcoded inputs will appear as optional to override the values entered here
+   Array[File] rawJsonRadiomicsFiles
+
+   #Docker Images for each task
+   String docker_radiomicsJsonToDataFrame = "imagingdatacommons/convert_radiomics_features_json_to_df:feat-convert-raw-radiomics-to-dataframe"
+
+   #Preemptible retries
+   Int preemptibleTries_radiomicsJsonToDataFrame= 3
+
+   #Compute CPU configuration
+   Int cpus_radiomicsJsonToDataFrame = 4
+   Int ram_radiomicsJsonToDataFrame = 16
+   String cpuFamily_radiomicsJsonToDataFrame = 'AMD Rome' 
+   
+   #Compute Zones
+   String zones_radiomicsJsonToDataFrame = "us-central1-a us-central1-b us-central1-c us-central1-f"
+
+ }
+ #calling Papermill Task with the inputs
+ call radiomicsJsonToDataFrame{
+   input:
+    rawJsonRadiomicsFiles = rawJsonRadiomicsFiles,
+    docker_radiomicsJsonToDataFrame = docker_radiomicsJsonToDataFrame,
+    preemptibleTries_radiomicsJsonToDataFrame = preemptibleTries_radiomicsJsonToDataFrame,
+    cpus_radiomicsJsonToDataFrame = cpus_radiomicsJsonToDataFrame,
+    ram_radiomicsJsonToDataFrame = ram_radiomicsJsonToDataFrame,
+    cpuFamily_radiomicsJsonToDataFrame = cpuFamily_radiomicsJsonToDataFrame, 
+    zones_radiomicsJsonToDataFrame = zones_radiomicsJsonToDataFrame
+}
+
+ output {
+  #output notebooks
+  
+   File outputNotebook_radiomicsJsonToDataFrame = radiomicsJsonToDataFrame.outputNotebook_radiomicsJsonToDataFrame
+   File parquet_radiomicsJsonToDataFrame = radiomicsJsonToDataFrame.parquet_radiomicsJsonToDataFrame 
+   File? conversion_Errors = radiomicsJsonToDataFrame.conversion_Errors
+}
+}
+#Task Definitions
+task radiomicsJsonToDataFrame{
+ input {
+   #Just like the workflow inputs, any new inputs entered here but not hardcoded will appear in the UI as required fields
+   #And the hardcoded inputs will appear as optional to override the values entered here
+    Array[File] rawJsonRadiomicsFiles
+    #Docker Images for task
+    String docker_radiomicsJsonToDataFrame
+    #Preemptible retries
+    Int preemptibleTries_radiomicsJsonToDataFrame
+    #Compute CPU configuration
+    Int cpus_radiomicsJsonToDataFrame
+    Int ram_radiomicsJsonToDataFrame
+    String cpuFamily_radiomicsJsonToDataFrame 
+    #Compute Zones
+    String zones_radiomicsJsonToDataFrame
+ }
+ command {
+   wget https://raw.githubusercontent.com/vkt1414/CloudSegmentator/feat-convert-raw-radiomics-to-dataframe/workflows/TotalSegmentator/Notebooks/postProcessingRadiomicsJsonToDataFrame.ipynb
+   
+   set -o xtrace
+   # For any command failures in the rest of this script, return the error.
+   set -o pipefail
+   set +o errexit
+   
+   papermill postProcessingRadiomicsJsonToDataFrame.ipynb outputNotebook_postProcessingRadiomicsJsonToDataFrame.ipynb -p rawJsonRadiomicsFiles ~{sep="," rawJsonRadiomicsFiles}
+   
+   set -o errexit
+   exit $?
+ }
+
+ #Run time attributes:
+ runtime {
+   docker: docker_radiomicsJsonToDataFrame
+   cpu: cpus_radiomicsJsonToDataFrame
+   cpuPlatform: cpuFamily_radiomicsJsonToDataFrame
+   zones: zones_radiomicsJsonToDataFrame
+   memory: ram_radiomicsJsonToDataFrame + " GiB"
+   disks: "local-disk 10 HDD" 
+   preemptible: preemptibleTries_radiomicsJsonToDataFrame
+   maxRetries: 1
+ }
+ output {
+   File outputNotebook_radiomicsJsonToDataFrame = "outputNotebook_postProcessingRadiomicsJsonToDataFrame.ipynb"
+   File parquet_radiomicsJsonToDataFrame  = "raw_radiomics.parquet"
+   File? conversion_Errors = "error_file.txt"
+ }
+}