| | --- |
| | license: mit |
| | task_categories: |
| | - image-segmentation |
| | language: |
| | - en |
| | tags: |
| | - medical |
| | pretty_name: AeroPath |
| | size_categories: |
| | - 1B<n<10B |
| | --- |
| | |
| |
|
| | <div align="center"> |
| | <h1 align="center">🫁 LyNoS 🤗</h1> |
| | <h3 align="center">A multilabel lymph node segmentation dataset from contrast CT</h3> |
| |
|
| | **LyNoS** was developed by SINTEF Medical Image Analysis to accelerate medical AI research. |
| |
|
| | </div> |
| |
|
| | ## [Brief intro](https://github.com/raidionics/LyNoS#brief-intro) |
| |
|
| | 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 dataset has now also been uploaded to Zenodo and the Hugging Face Hub enabling users to more easily access the data through Python API. |
| |
|
| | 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. |
| |
|
| | ## [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> |
| |
|
| | ### [Accessing dataset](https://github.com/raidionics/LyNoS#accessing-dataset) |
| |
|
| | The dataset contains 15 CTs with corresponding lymph nodes, azygos, esophagus, and subclavian carotid arteries. The folder structure is described below. |
| |
|
| | The easiest way to access the data is through Python with Hugging Face's [datasets](https://pypi.org/project/datasets/) package: |
| | ``` |
| | from datasets import load_dataset |
| | |
| | # downloads data from Zenodo through the Hugging Face hub |
| | # - might take several minutes (~5 minutes in CoLab) |
| | dataset = load_dataset("andreped/LyNoS") |
| | print(dataset) |
| | |
| | # list paths of all available patients and corresponding features (ct/lymphnodes/azygos/brachiocephalicveins/esophagus/subclaviancarotidarteries) |
| | for d in dataset["test"]: |
| | print(d) |
| | ``` |
| |
|
| | 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/274bf953771059fd9537877404369bed/lynos-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/LyNoS/blob/main/notebooks/lynos-load-dataset-example.ipynb) to see it on GitHub. |
| |
|
| |
|
| | ### [Dataset structure](https://github.com/raidionics/LyNoS#dataset-structure) |
| |
|
| | ``` |
| | └── LyNoS.zip |
| | ├── stations_sto.csv |
| | └── LyNoS/ |
| | ├── Pat1/ |
| | │ ├── pat1_data.nii.gz |
| | │ ├── pat1_labels_Azygos.nii.gz |
| | │ ├── pat1_labels_Esophagus.nii.gz |
| | │ ├── pat1_labels_LymphNodes.nii.gz |
| | │ └── pat1_labels_SubCarArt.nii.gz |
| | ├── [...] |
| | └── Pat15/ |
| | ├── pat15_data.nii.gz |
| | ├── pat15_labels_Azygos.nii.gz |
| | ├── pat15_labels_Esophagus.nii.gz |
| | ├── pat15_labels_LymphNodes.nii.gz |
| | └── pat15_labels_SubCarArt.nii.gz |
| | ``` |
| |
|
| | ### [NIH Dataset Completion](https://github.com/raidionics/LyNoS#nih-dataset-completion) |
| | A larger dataset made of 90 patients featuring enlarged lymph nodes has also been made available by the National Institutes of Health, and is available for download on the official [web-page](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=19726546). |
| | As a supplement to this dataset, lymph nodes segmentation masks have been refined for all patients and stations have been manually assigned to each, available [here](https://drive.google.com/uc?id=1iVCnZc1GHwtx9scyAXdANqz2HdQArTHn). |
| |
|
| | ## [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> |
| |
|
| | To access the live demo, click on the `Hugging Face` badge above. Below is a snapshot of the current state of the demo app. |
| |
|
| | <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"> |
| |
|
| | ## [Development](https://github.com/raidionics/LyNoS#development) |
| |
|
| | ### [Docker](https://github.com/raidionics/LyNoS#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](https://github.com/raidionics/LyNoS#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](https://github.com/raidionics/LyNoS#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](https://github.com/raidionics/LyNoS#license) |
| |
|
| | The code in this repository is released under [MIT license](https://github.com/raidionics/LyNoS/blob/main/LICENSE). |
