DECIDE Docker

This repository contains scripts and configuration files to build and run a Docker container for image segmentation, post-editing, and (soon) anomaly detection as part of the DECIDE project.

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🚀 Getting Started

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NB: If you are using cloud computing and port forwarding, please refer to:
Cloud Compute Deployment (In ARGOS-1 directory for XNAT) for additional setup instructions.

1. Setup

• Clone the repository to your machine:

  git clone https://github.com/NKI-RT/DecideDocker.git

• Navigate into the directory:

  cd DecideDocker

• Configure environment variables:
  • Edit the workspace/.env file (make sure hidden files are visible).
  • Or, rename default.env to .env.

2. Run the Container

Start the container using Docker Compose:

sudo docker compose up -d

🔄 Note:
The container will perform runtime installations and builds via the startup.sh script. This may take a few minutes.

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✅ Access JupyterLab

• URL:
  Open http://<your_ip_address>:8888 in your browser.
• Login Token:
  Use the configured token (default: token123).

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⏹ Stop the Container

To stop the container:

sudo docker compose down

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🕰 Legacy Docker Compose (Standalone Tool)

For environments using the deprecated standalone docker-compose tool, use:

Start:

sudo docker-compose -f docker-compose-legacy.yml up -d

Stop:

sudo docker-compose -f docker-compose-legacy.yml down

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☁️ Cloud Compute Deployment (In ARGOS-1 dir. for the XNAT)

If you're using cloud computing and your virtual machine's IP address is not public, follow these steps to deploy XNAT and Decide together using a shared proxy network.

📦 Setup Instructions

1. Stop the existing XNAT container (of ARGOS-1):

   # in ARGOS-1
   cd xnat-docker-compose

   # stop the XNAT
   sudo docker compose down

2. Download this repository into the same directory.:

   git clone https://github.com/NKI-RT/DecideDocker.git

3. Copy the cloud compute Docker Compose file:

   cp DecideDocker/docker-compose-cloudcompute.yml ./

4. Run the integrated deployment:

   sudo docker compose -f docker-compose-cloudcompute.yml up -d

5. To stop the deployment:

   sudo docker compose -f docker-compose-cloudcompute.yml down

Congratulations! You can now access your XNAT at: http://xnat-nginx:80

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🔗 Notes

• This setup runs XNAT, Decide, and NGINX in a shared proxynet Docker network.
• The container will expose:
  • JupyterLab (Decide) on port 8888
  • XNAT NGINX proxy on port 80

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🔄 Staying Up to Date (Force Sync with Upstream)

To update your local copy with the latest changes from the original repository and discard any local changes:

1. Add the original repo as a remote (if not already):

   git remote add upstream https://github.com/NKI-RT/DecideDocker.git

2. Fetch the latest changes from upstream:

   git fetch upstream

3. Reset your local main branch to match upstream/main:

   git checkout main
   git reset --hard upstream/main

4. (Optional) Clean up untracked files and directories:

   git clean -fd

⚠️ Warning: This will permanently discard all local changes, including uncommitted edits and untracked files.

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📢 Updates

We will release more features and scripts/notebooks as the project progresses.
Stay tuned and keep your local copy up to date!

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📁 Repository Structure

DecideDocker/
├── Dockerfile
├── docker-compose.yml
├── docker-compose-legacy.yml
├── docker-compose-cloudcompute.yml
├── startup.sh
├── workspace/
│   ├── .env (default.env)
│   ├── decide/
│   │   ├── config/
│   │   │   ├── config_total_segmentator.yaml
│   │   │   └── dicomdata_config.yaml
│   │   ├── logs/
│   │   ├── data/
│   │   |   └── LUNG1-001/
│   │   |       ├── CT
│   │   |       ├── RTSTRUCT
│   │   |       └── NIfTI
│   │   ├── src/
│   │   │   └── decide/
│   │   └── pyproject.toml
│   ├── notebooks/
│   │   ├── 001_test_essentials.ipynb
│   │   ├── 002_get_nifti.ipynb
│   |   └── 002_get_niftiv2.ipynb
│   └── scripts/
│       └── get_nifti.py
└── nnUNet/

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🛠 Dockerfile Overview

The Dockerfile uses a multi-stage build:

Stage 1: JupyterLab Setup

• Base: ubuntu:22.04
• Installs latest JupyterLab and extensions

Stage 2: DECIDE Environment

• Base: TotalSegmentator image
• Adds JupyterLab from Stage 1
• Installs:
  • Plastimatch
  • PyRadiomics
  • nnUNet patch for Plastimatch
  • DECIDE tools (including XNAT Python API)
• Resolves dependency issues
• Launches JupyterLab on port 8888

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⚙️ docker-compose.yml

Defines container behavior:

• Uses host IPC (required for nnUNet models)
• Enables GPU support
• Mounts workspace/ directory for live sync between host and container

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📜 startup.sh

The startup.sh script is executed when the container starts. It performs the following tasks:

• Builds and installs Plastimatch from source
• Installs required Python packages (e.g., PyRadiomics, Platipy)
• Applies a patch to nnUNet for compatibility
• Installs DECIDE tools if present in /workspace/decide
• Fixes specific package versions (e.g., numpy, pydicom)
• Registers the Jupyter kernel
• Configures and launches JupyterLab on port 8888

🔐 .env File

Stores environment variables:

JUPYTER_TOKEN=token123

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📦 Workspace

• Mounted inside the container
• Contains all scripts, notebooks, and configurations
• src/ includes Python packages for segmentation, post-editing, etc.

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🧠 nnUNet Compatibility Fixes

Common Errors

When installed platipy platipy[nnunet] or platipy[cardiac] and deployed, encountered the error:

UnpicklingError: Weights only load failed...

Cause

PyTorch 2.6 changed the default weights_only argument in torch.load to True.

Fix

1. Use nnUNet version 1.7.0:

   git clone https://github.com/MIC-DKFZ/nnUNet.git
   cd nnUNet
   git checkout tags/v1.7.0 -b nnunet-1.7.0

2. Patch model_restore.py:

   from torch.serialization import safe_globals
   import numpy
   
   with safe_globals([numpy.dtype]):
       all_params = [torch.load(i, map_location=torch.device('cpu'), weights_only=False) for i in all_best_model_files]

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🧱 Architecture Comparison

ARGOS-1

• Single Docker Compose managing XNAT, JupyterLab, and tools

• Adding new containers required editing XNAT's Docker Compose

  

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DECIDE

• Separation of data containers and tool containers

• No modification needed to data containers when adding/removing tools

  

Test Data Attribution

This repository includes data derived from the NSCLC-Radiomics dataset available via the Imaging Data Commons (IDC).

Original dataset citation: The Cancer Imaging Archive. https://www.cancerimagingarchive.net/collection/nsclc-radiomics/

License: CC BY-NC 3.0 — Non-commercial use only.