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 web application enables users to easily test our deep learning model for airway segmentation in CTs. The plugin is built on top of gradio using the same backend as used for the Raidionics software. Raidionics is an open-source, free-to-use desktop application for pre- and postoperative central nervous system tumor segmentation and standardized reporting, but the same core backend principles can easily be adapted to other applications, as demonstrated in this repository.

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 this tool relevant in your research, please cite the following reference which introduced the backend that is used for the AeroPath demonstration:

@article{bouget2023raidionics,
    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 = {},
    title = {Raidionics: an open software for pre-and postoperative central nervous system tumor segmentation and standardized reporting},
    volume = {13},
    journal = {Scientific Reports},
    doi = {10.1038/s41598-023-42048-7},
}