monailabel-weekly 0.1.dev2126

Active Learning Toolkit for Healthcare Imaging

MONAILabel

MONAILabel is a server-client system that facilitates interactive medical image annotation by using AI. It is an open-source and easy-to-install ecosystem that can run locally on a machine with one or two GPUs. Both server and client work on the same/different machine. However, initial support for multiple users is restricted. It shares the same principles with MONAI.

Brief Demo

Development in Progress. We will be actively working on this repository to add more features, fix issues, update docs, readme etc... as we make more progress. Wiki's, LICENSE, Contributions, Code Compliance, CI Tool Integration etc... Otherwise, it shares the same principles with MONAI.

Features

The codebase is currently under active development.

• framework for developing and deploying MONAILabel Apps to train and infer AI models
• compositional & portable APIs for ease of integration in existing workflows
• customizable design for varying user expertise
• 3D slicer support

Installation

MONAILabel supports following OS with GPU/CUDA enabled.

Ubuntu

  # One time setup (to pull monai with nvidia gpus)
  docker run -it --rm --gpus all --ipc=host --net=host -v /rapid/xyz:/workspace/ projectmonai/monai:0.5.2
  
  # Install monailabel 
  pip install git+https://github.com/Project-MONAI/MONAILabel#egg=monailabel
  
  # Download MSD Datasets
  monailabel datasets # list sample datasets
  monailabel datasets --download --name Task02_Heart --output /workspace/datasets/
  
  # Download Sample Apps
  monailabel apps # list sample apps
  monailabel apps --download --name deepedit_left_atrium --output /workspace/apps/
  
  # Start Server
  monailabel start_server --app /workspace/apps/deepedit_left_atrium --studies /workspace/datasets/Task02_Heart/imagesTr

Windows

Pre Requirements

Make sure you have python 3.x version environment with PyTorch + CUDA installed.

• Install python
• Install cuda (Faster mode: install CUDA runtime only)
• python -m pip install --upgrade pip setuptools wheel
• pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
• python -c "import torch; print(torch.cuda.is_available())"

MONAILabel

  pip install git+https://github.com/Project-MONAI/MONAILabel#egg=monailabel
  monailabel -h
  
  # Download MSD Datasets
  monailabel datasets # List sample datasets
  monailabel datasets --download --name Task02_Heart --output C:\Workspace\Datasets
  
  # Download Sample Apps
  monailabel apps # List sample apps
  monailabel apps --download --name deepedit_left_atrium --output C:\Workspace\Apps
  
  # Start Server
  monailabel start_server --app C:\Workspace\Apps\deepedit_left_atrium --studies C:\Workspace\Datasets\Task02_Heart\imagesTr

Once you start the MONAILabel Server, by default it will be up and serving at http://127.0.0.1:8000/. Open the serving URL in browser. It will provide you the list of Rest APIs available.

3D Slicer

Refer 3D Slicer plugin for installing and running MONAILabel plugin in 3D Slicer.

Contributing

For guidance on making a contribution to MONAILabel, see the contributing guidelines.

Community

Join the conversation on Twitter @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAILabel's GitHub Discussions tab.

Links

• Website: https://monai.io/
• API documentation: https://docs.monai.io/monailabel
• Code: https://github.com/Project-MONAI/MONAILabel
• Project tracker: https://github.com/Project-MONAI/MONAILabel/projects
• Issue tracker: https://github.com/Project-MONAI/MONAILabel/issues
• Wiki: https://github.com/Project-MONAI/MONAILabel/wiki
• Test status: https://github.com/Project-MONAI/MONAILabel/actions
• PyPI package: https://pypi.org/project/monailabel/
• Weekly previews: https://pypi.org/project/monailabel-weekly/
• Docker Hub: https://hub.docker.com/r/projectmonai/monailabel