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--- |
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title: 'LyNoS: automatic lymph node segmentation using deep learning' |
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colorFrom: indigo |
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colorTo: indigo |
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sdk: docker |
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app_port: 7860 |
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emoji: 🫁 |
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pinned: false |
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license: mit |
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app_file: demo/app.py |
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--- |
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<div align="center"> |
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<h1 align="center">🫁 LyNoS 🤗</h1> |
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<h3 align="center">A lymph node segmentation benchmark from contrast CT</h3> |
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[](https://github.com/raidionics/LyNoS/blob/main/LICENSE.md) |
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[](https://github.com/raidionics/LyNoS/actions/workflows/deploy.yml) |
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<a target="_blank" href="https://huggingface.co/spaces/andreped/LyNoS"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a> |
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[](https://doi.org/10.1080/21681163.2022.2043778) |
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**LyNoS** was developed by SINTEF Medical Image Analysis to accelerate medical AI research. |
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</div> |
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## [Brief intro](https://github.com/raidionics/LyNoS#brief-intro) |
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This repository contains the LyNoS dataset described in ["_Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding_"](https://doi.org/10.1080/21681163.2022.2043778). The original pretrained model was made openly available [here](https://github.com/dbouget/ct_mediastinal_structures_segmentation). However, we have gone ahead and made a web demonstration to more easily test the pretrained model. The application was developed using [Gradio](https://www.gradio.app) for the frontend and the segmentation is performed using the [Raidionics](https://raidionics.github.io/) backend. |
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## [Continuous integration](https://github.com/raidionics/LyNoS#continuous-integration) |
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| Build Type | Status | |
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| - | - | |
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| **HF Deploy** | [](https://github.com/raidionics/LyNoS/actions) | |
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| **File size check** | [](https://github.com/raidionics/LyNoS/actions) | |
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| **Formatting check** | [](https://github.com/raidionics/LyNoS/actions) | |
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## [Development](https://github.com/raidionics/LyNoS#development) |
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### [Docker](https://github.com/raidionics/LyNoS#docker) |
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Alternatively, you can deploy the software locally. Note that this is only relevant for development purposes. Simply dockerize the app and run it: |
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``` |
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docker build -t LyNoS . |
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docker run -it -p 7860:7860 LyNoS |
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``` |
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Then open `http://127.0.0.1:7860` in your favourite internet browser to view the demo. |
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### [Python](https://github.com/raidionics/LyNoS#python) |
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It is also possible to run the app locally without Docker. Just setup a virtual environment and run the app. |
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Note that the current working directory would need to be adjusted based on where `LyNoS` is located on disk. |
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``` |
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git clone https://github.com/raidionics/LyNoS.git |
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cd LyNoS/ |
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virtualenv -python3 venv --clear |
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source venv/bin/activate |
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pip install -r ./demo/requirements.txt |
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python demo/app.py --cwd ./ |
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``` |
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## [Citation](https://github.com/raidionics/LyNoS#citation) |
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If you found the dataset and/or web application relevant in your research, please cite the following reference: |
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``` |
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@article{bouget2021mediastinal, |
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author = {David Bouget and André Pedersen and Johanna Vanel and Haakon O. Leira and Thomas Langø}, |
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title = {Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding}, |
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journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization}, |
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volume = {0}, |
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number = {0}, |
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pages = {1-15}, |
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year = {2022}, |
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publisher = {Taylor & Francis}, |
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doi = {10.1080/21681163.2022.2043778}, |
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URL = {https://doi.org/10.1080/21681163.2022.2043778}, |
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eprint = {https://doi.org/10.1080/21681163.2022.2043778} |
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} |
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``` |
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## [License](https://github.com/raidionics/LyNoS#license) |
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The code in this repository is released under [MIT license](https://github.com/raidionics/LyNoS/blob/main/LICENSE.md). |
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