Active Learning Toolkit for Healthcare Imaging
Project description
MONAI Label
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 single or multiple GPUs. Both server and client work on the same/different machine. It shares the same principles with MONAI.
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 labelling app design for varying user expertise
- Annotation support via 3DSlicer & OHIF
- PACS connectivity via DICOMWeb
Installation
MONAI Label supports following OS with GPU/CUDA enabled.
- Ubuntu
- Windows
To install the current release, you can simply run:
pip install monailabel
# download sample apps/dataset
monailabel apps --download --name deepedit --output apps
monailabel datasets --download --name Task09_Spleen --output datasets
# run server
monailabel start_server --app apps/deepedit --studies datasets/Task09_Spleen/imagesTr
If monailabel install path is not automatically determined, then you can provide explicit install path as:
monailabel apps --prefix ~/.local
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 server 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.
To avoid accidentally using an older Slicer version, you may want to uninstall any previously installed 3D Slicer package.
OHIF
MONAI Label comes with pre-built plugin for OHIF Viewer. To use OHIF Viewer, you need to provide DICOMWeb instead of FileSystem as studies when you start the server.
Please install Orthanc before using OHIF Viewer. For Ubuntu 20.x, Orthanc can be installed as
apt-get install orthanc orthanc-dicomweb. However, you have to upgrade to latest version by following steps mentioned hereYou can use PlastiMatch to convert NIFTI to DICOM
# start server using DICOMWeb
monailabel start_server --app apps\deepedit --studies http://127.0.0.1:8042/dicom-web
OHIF Viewer will be accessible at http://127.0.0.1:8000/ohif/
NOTE: OHIF does not yet support Scribbles-based annotations and Multi-Label interaction for DeepEdit.
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
- Website: https://monai.io/
- API documentation: https://docs.monai.io/projects/label
- 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-hypernode.com/project/monailabel/
- Weekly previews: https://pypi-hypernode.com/project/monailabel-weekly/
- Docker Hub: https://hub.docker.com/r/projectmonai/monailabel
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
File details
Details for the file monailabel-0.3.0-202111281134-py3-none-any.whl.
File metadata
- Download URL: monailabel-0.3.0-202111281134-py3-none-any.whl
- Upload date:
- Size: 5.1 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d2a30d1701e0cc8764b4ecf251874bc07a2ebec3dcb522f87777c4bd5b67023b |
|
| MD5 | c53b0cc672390b8abdea2e2de61b1ed6 |
|
| BLAKE2b-256 | 0e192ee64367c3bddf819ae986dc379f063b72efe5c159f71a3ce79b4ed34a17 |
