metadata

title: 'AeroPath: automatic airway segmentation using deep learning'
colorFrom: indigo
colorTo: indigo
sdk: docker
app_port: 7860
emoji: 🫁
pinned: false
license: mit
app_file: demo/app.py

AeroPath

automatic airway segmentation using deep learning

AeroPath was developed by SINTEF Medical Image Analysis to accelerate medical AI research.

Brief intro

This repository contains the AeroPath dataset described in "AeroPath: An airway segmentation benchmark dataset with challenging pathology". A web application was also developed in the study, to enable users to easily test our deep learning model on their own data. The application was developed using Gradio for the frontend and the segmentation is performed using the Raidionics backend.

The dataset can be accessed from Releases.

Demo

To access the live demo, click on the Hugging Face badge above. Below is a snapshot of the current state of the demo app.

Continuous integration

Build Type	Status
HF Deploy
File size check
Formatting check

Development

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 AeroPath .
docker run -it -p 7860:7860 AeroPath

Then open http://127.0.0.1:7860 in your favourite internet browser to view the demo.

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 AeroPath is located on disk.

git clone https://github.com/raidionics/AeroPath.git
cd AeroPath/

virtualenv -python3 venv --clear
source venv/bin/activate
pip install -r ./demo/requirements.txt

python demo/app.py --cwd ./

Citation

If you found the dataset and/or web application relevant in your research, please cite the following reference:

@misc{støverud2023aeropath,
      title={{AeroPath: An airway segmentation benchmark dataset with challenging pathology}}, 
      author={Karen-Helene Støverud and David Bouget and Andre Pedersen and Håkon Olav Leira and Thomas Langø and Erlend Fagertun Hofstad},
      year={2023},
      eprint={2311.01138},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

The web application is using the Raidionics backend, thus, also consider citing:

@article{bouget2023raidionics,
    title = {Raidionics: an open software for pre-and postoperative central nervous system tumor segmentation and standardized reporting},
    author = {Bouget, David and Alsinan, Demah and Gaitan, Valeria and Holden Helland, Ragnhild and Pedersen, André and Solheim, Ole and Reinertsen, Ingerid},
    year = {2023},
    month = {09},
    pages = {},
    volume = {13},
    journal = {Scientific Reports},
    doi = {10.1038/s41598-023-42048-7},
}

License

The code in this repository is released under MIT license.