Merge pull request #5 from andreped/demo

LICENSE.md

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 André Pedersen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -21,4 +21,68 @@ app_file: demo/app.py
 
 **LyNoS** was developed by SINTEF Medical Image Analysis to accelerate medical AI research.
 
-</div>
+</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 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.
+
+## [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.md).