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.
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 ./
Troubleshooting
Due to share=True being enabled by default when launching the app,
internet access is required for the app to be launched. This can disabled by setting
the argument to --share 0.
Citation
If you found this tool relevant in your research, please cite the following references enabling the backend compute magic.
The final software including updated performance metrics for preoperative tumors and introducing postoperative tumor segmentation:
@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},
}
For the preliminary preoperative tumor segmentation validation and software features:
@article{bouget2022preoptumorseg,
title={Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting},
author={Bouget, David and Pedersen, André and Jakola, Asgeir S. and Kavouridis, Vasileios and Emblem, Kyrre E. and Eijgelaar, Roelant S. and Kommers, Ivar and Ardon, Hilko and Barkhof, Frederik and Bello, Lorenzo and Berger, Mitchel S. and Conti Nibali, Marco and Furtner, Julia and Hervey-Jumper, Shawn and Idema, Albert J. S. and Kiesel, Barbara and Kloet, Alfred and Mandonnet, Emmanuel and Müller, Domenique M. J. and Robe, Pierre A. and Rossi, Marco and Sciortino, Tommaso and Van den Brink, Wimar A. and Wagemakers, Michiel and Widhalm, Georg and Witte, Marnix G. and Zwinderman, Aeilko H. and De Witt Hamer, Philip C. and Solheim, Ole and Reinertsen, Ingerid},
journal={Frontiers in Neurology},
volume={13},
year={2022},
url={https://www.frontiersin.org/articles/10.3389/fneur.2022.932219},
doi={10.3389/fneur.2022.932219},
issn={1664-2295}
}