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 dataset from contrast CT
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.
Demo
To access the live demo, click on the Hugging Face badge above. Below is a snapshot of the current state of the demo app.
Continuous integration
| Build Type | Status |
|---|---|
| HF Deploy |  |
| File size check |  |
| Formatting check |  |
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.