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.

Metadata

Release files for monailabel-weekly 0.1.dev2125

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for monailabel-weekly 0.1.dev2125
File	Size	Uploaded	Provenance
monailabel-weekly-0.1.dev2125.tar.gz	74.0 kB	Jun 22, 2021	Without provenance	Details

Built distribution (wheel)

Table of built distributions (wheels) for monailabel-weekly 0.1.dev2125
File	Interpreter	ABI	Platform	Reset
monailabel_weekly-0.1.dev2125-py3-none-any.whl 79.3 kB Jun 22, 2021 Without provenance	Python 3	none	any	Details

Total release size: 153.3 kB

Release files / monailabel-weekly-0.1.dev2125.tar.gz

Download URL	monailabel-weekly-0.1.dev2125.tar.gz
Size	74.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`a5f4bd2a1eb89989dde7149f2599fc462edf95bc04470800d89cdb5f085a2b55`
BLAKE2b-256 checksum How to use checksums	`c00397648ce42fe7668aa0c8f569b0bf85fb42424487e6c384e124a9d960764e`
Upload date	Jun 22, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.4.1 importlib_metadata/2.0.0 pkginfo/1.5.0.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.7.10`

Release files / monailabel_weekly-0.1.dev2125-py3-none-any.whl

Download URL	monailabel_weekly-0.1.dev2125-py3-none-any.whl
Size	79.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`e62c6c5df521e29f59ee71c8740796443a144f636ec3aa1c1f65d59af054bb93`
BLAKE2b-256 checksum How to use checksums	`21f345c96beb6a37eeb8039459eac6ad09797e955bd8dd134719f9d55e03867e`
Upload date	Jun 22, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.9.5`

Security for monailabel-weekly 0.1.dev2125

Report project as malware

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.