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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 dataset 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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<a href="https://colab.research.google.com/gist/andreped/274bf953771059fd9537877404369bed/lynos-load-dataset-example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></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). |
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The dataset has now also been uploaded to Zenodo and the Hugging Face Hub enabling users to more easily access the data through Python API. |
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We have also developed a web demo to enable others to easily test the pretrained model presented in the paper. 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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## [Dataset](https://github.com/raidionics/LyNoS#data) <a href="https://colab.research.google.com/gist/andreped/274bf953771059fd9537877404369bed/lynos-load-dataset-example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
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### [Accessing dataset](https://github.com/raidionics/LyNoS#accessing-dataset) |
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The dataset contains 15 CTs with corresponding lymph nodes, azygos, esophagus, and subclavian carotid arteries. The folder structure is described below. |
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The easiest way to access the data is through Python with Hugging Face's [datasets](https://pypi.org/project/datasets/) package: |
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``` |
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from datasets import load_dataset |
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# downloads data from Zenodo through the Hugging Face hub |
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# - might take several minutes (~5 minutes in CoLab) |
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dataset = load_dataset("andreped/LyNoS") |
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print(dataset) |
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# list paths of all available patients and corresponding features (ct/lymphnodes/azygos/brachiocephalicveins/esophagus/subclaviancarotidarteries) |
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for d in dataset["test"]: |
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print(d) |
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``` |
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A detailed interactive demo on how to load and work with the data can be seen on CoLab. Click the CoLab badge <a href="https://colab.research.google.com/gist/andreped/6070d1d2914a9ce5847d4b3e687188b7/aeropath-load-dataset-example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> to see the notebook or alternatively click [here](https://github.com/raidionics/AeroPath/blob/main/notebooks/aeropath-load-dataset-example.ipynb) to see it on GitHub. |
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### [Dataset structure](https://github.com/raidionics/LyNoS#dataset-structure) |
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``` |
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└── AeroPath.zip |
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├── stations_sto.csv |
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└── AeroPath/ |
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├── Pat1/ |
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│ ├── pat1_data.nii.gz |
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│ ├── pat1_labels_Azygos.nii.gz |
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│ ├── pat1_labels_Esophagus.nii.gz |
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│ ├── pat1_labels_LymphNodes.nii.gz |
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│ └── pat1_labels_SubCarArt.nii.gz |
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├── [...] |
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└── Pat15/ |
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├── pat15_data.nii.gz |
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├── pat15_labels_Azygos.nii.gz |
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├── pat15_labels_Esophagus.nii.gz |
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├── pat15_labels_LymphNodes.nii.gz |
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└── pat15_labels_SubCarArt.nii.gz |
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``` |
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## [Demo](https://github.com/raidionics/LyNoS#demo) <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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To access the live demo, click on the `Hugging Face` badge above. Below is a snapshot of the current state of the demo app. |
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<img width="1400" alt="Screenshot 2023-11-09 at 20 53 29" src="https://github.com/raidionics/LyNoS/assets/29090665/ce661da0-d172-4481-b9b5-8b3e29a9fc1f"> |
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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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