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Active Learning Toolkit for Healthcare Imaging

Project description

MONAI Label

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MONAI Label 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

Features

The codebase is currently under active development.

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

Installation

MONAI Label supports following OS with GPU/CUDA enabled.

To install the current release, you can simply run:

  pip install monailabel
  
  # download sample apps/dataset
  monailabel apps --download --name deepedit_left_atrium --output apps
  monailabel datasets --download --name Task02_Heart --output datasets
  
  # run server
  monailabel start_server --app apps\deepedit_left_atrium --studies datasets\Task02_Heart\imagesTr
  

For prerequisites, other installation methods (using the default GitHub branch, using Docker, etc.), please refer to the installation guide.

Once you start the MONAI Label 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

Download Preview Release from https://download.slicer.org/ and install MONAI Label plugin from Slicer Extension Manager.

Refer 3D Slicer plugin for other options to install and run MONAI Label plugin in 3D Slicer.

Contributing

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

Community

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

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

Links

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