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metadata
title: 'LyNoS: automatic lymph node segmentation using deep learning'
colorFrom: indigo
colorTo: indigo
sdk: docker
app_port: 7860
emoji: 🫁
pinned: false
license: mit
app_file: demo/app.py

🫁 LyNoS 🤗

A lymph node segmentation benchmark from contrast CT

license CI/CD paper

LyNoS was developed by SINTEF Medical Image Analysis to accelerate medical AI research.

Brief intro

This repository contains the LyNoS dataset described in "Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding". The original pretrained model was made openly available here. However, we have gone ahead and made a web demonstration to more easily test the pretrained model. The application was developed using Gradio for the frontend and the segmentation is performed using the Raidionics backend.

Continuous integration

Build Type Status
HF Deploy Deploy
File size check Filesize
Formatting check Filesize

Development

Docker

Alternatively, you can deploy the software locally. Note that this is only relevant for development purposes. Simply dockerize the app and run it:

docker build -t LyNoS .
docker run -it -p 7860:7860 LyNoS

Then open http://127.0.0.1:7860 in your favourite internet browser to view the demo.

Python

It is also possible to run the app locally without Docker. Just setup a virtual environment and run the app. Note that the current working directory would need to be adjusted based on where LyNoS is located on disk.

git clone https://github.com/raidionics/LyNoS.git
cd LyNoS/

virtualenv -python3 venv --clear
source venv/bin/activate
pip install -r ./demo/requirements.txt

python demo/app.py --cwd ./

Citation

If you found the dataset and/or web application relevant in your research, please cite the following reference:

@article{bouget2021mediastinal,
  author = {David Bouget and André Pedersen and Johanna Vanel and Haakon O. Leira and Thomas Langø},
  title = {Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding},
  journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
  volume = {0},
  number = {0},
  pages = {1-15},
  year  = {2022},
  publisher = {Taylor & Francis},
  doi = {10.1080/21681163.2022.2043778},
  URL = {https://doi.org/10.1080/21681163.2022.2043778},
  eprint = {https://doi.org/10.1080/21681163.2022.2043778}
}

License

The code in this repository is released under MIT license.