| title | colorFrom | colorTo | sdk | app_port | emoji | pinned | license | app_file |
|---|---|---|---|---|---|---|---|---|
AeroPath: automatic airway segmentation using deep learning |
indigo |
indigo |
docker |
7860 |
🫁 |
false |
mit |
demo/app.py |
AeroPath was developed by SINTEF Medical Image Analysis to accelerate medical AI research.
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 is made openly available at Zenodo and the Hugging Face Hub. Click any of the two hyperlinks to access the dataset.
The dataset contains 27 CTs with corresponding airways and lung annotations. The folder structure is described below.
The easiest way to access the data is in Python with Hugging Face's 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/AeroPath")
print(dataset)
# list paths of all available patients and corresponding features (ct/airways/lungs)
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
└── AeroPath.zip
├── README.md
└── AeroPath/
├── 1/
│ ├── 1_CT_HR.nii.gz
│ ├── 1_CT_HR_label_airways.nii.gz
│ └── 1_CT_HR_label_lungs.nii.gz
├── [...]
└── 27/
├── 27_CT_HR.nii.gz
├── 27_CT_HR_label_airways.nii.gz
└── 27_CT_HR_label_lungs.nii.gz
To access the live demo, click on the Hugging Face badge above. Below is a snapshot of the current state of the demo app.
| Build Type | Status |
|---|---|
| HF Deploy |  |
| File size check |  |
| Formatting check |  |
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.
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 ./
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 dataset is hosted at Zenodo, so you should also cite the following:
@dataset{hofstad2023aeropathzenodo,
title = {{AeroPath: An airway segmentation benchmark dataset with challenging pathology}},
author = {Hofstad, Erlend and Bouget, David and Pedersen, André},
month = nov,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.10069289},
url = {https://doi.org/10.5281/zenodo.10069289}
}
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},
}
The code in this repository is released under MIT license.
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