Update README.md

README.md

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 ---
 license: mit
+task_categories:
+- image-segmentation
+language:
+- en
+tags:
+- medical
+pretty_name: AeroPath
+size_categories:
+- 1B<n<10B
 ---
+
+---
+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
+---
+
+<div align="center">
+<h1 align="center">🫁 LyNoS 🤗</h1>
+<h3 align="center">A multilabel lymph node segmentation dataset from contrast CT</h3>
+
+[![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/raidionics/LyNoS/blob/main/LICENSE.md)
+[![CI/CD](https://github.com/raidionics/LyNoS/actions/workflows/deploy.yml/badge.svg)](https://github.com/raidionics/LyNoS/actions/workflows/deploy.yml)
+<a target="_blank" href="https://huggingface.co/spaces/andreped/LyNoS"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a>
+<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>
+[![paper](https://img.shields.io/badge/paper-pdf-D12424)](https://doi.org/10.1080/21681163.2022.2043778)
+
+**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
+```
+
+## [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">
+
+## [Continuous integration](https://github.com/raidionics/LyNoS#continuous-integration)
+
+| Build Type | Status |
+| - | - |
+| **HF Deploy** | [![Deploy](https://github.com/raidionics/LyNoS/workflows/Deploy/badge.svg)](https://github.com/raidionics/LyNoS/actions) |
+| **File size check** | [![Filesize](https://github.com/raidionics/LyNoS/workflows/Check%20file%20size/badge.svg)](https://github.com/raidionics/LyNoS/actions) |
+| **Formatting check** | [![Filesize](https://github.com/raidionics/LyNoS/workflows/Linting/badge.svg)](https://github.com/raidionics/LyNoS/actions) |
+
+## [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).