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--- |
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title: 'AeroPath: automatic airway segmentation using deep learning' |
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colorFrom: indigo |
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colorTo: indigo |
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sdk: docker |
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app_port: 7860 |
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emoji: 🫁 |
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pinned: false |
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license: mit |
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app_file: demo/app.py |
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--- |
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<div align="center"> |
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<h1 align="center">🫁 AeroPath 🤗</h1> |
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<h3 align="center">An airway segmentation benchmark dataset with challenging pathology</h3> |
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[](https://github.com/raidionics/AeroPath/blob/main/LICENSE.md) |
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[](https://github.com/raidionics/AeroPath/actions/workflows/deploy.yml) |
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<a target="_blank" href="https://huggingface.co/spaces/andreped/AeroPath"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a> |
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<a href="https://colab.research.google.com/gist/andreped/6070d1d2914a9ce5847d4b3e687188b7/aeropath-load-dataset-example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
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[](https://doi.org/10.5281/zenodo.10069288) |
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[](https://arxiv.org/abs/2311.01138) |
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**AeroPath** was developed by SINTEF Medical Image Analysis to accelerate medical AI research. |
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</div> |
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## [Brief intro](https://github.com/raidionics/AeroPath#brief-intro) |
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This repository contains the AeroPath dataset described in ["_AeroPath: An airway segmentation benchmark dataset with challenging pathology_"](https://arxiv.org/abs/2311.01138). 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](https://www.gradio.app) for the frontend and the segmentation is performed using the [Raidionics](https://raidionics.github.io/) backend. |
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The dataset is made openly available at [Zenodo](https://zenodo.org/records/10069289) and [the Hugging Face Hub](https://huggingface.co/datasets/andreped/AeroPath). Click any of the two hyperlinks to access the dataset. |
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## [Dataset](https://github.com/raidionics/AeroPath#data) <a href="https://colab.research.google.com/gist/andreped/6070d1d2914a9ce5847d4b3e687188b7/aeropath-load-dataset-example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
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### [Accessing dataset](https://github.com/raidionics/AeroPath#accessing-dataset) |
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The dataset contains 27 CTs with corresponding airways and lung annotations. The folder structure is described below. |
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The easiest way to access the data is through Python with Hugging Face's [datasets](https://pypi.org/project/datasets/) package: |
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``` |
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from datasets import load_dataset |
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# downloads data from Zenodo through the Hugging Face hub |
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# - might take several minutes (~5 minutes in CoLab) |
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dataset = load_dataset("andreped/AeroPath") |
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print(dataset) |
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# list paths of all available patients and corresponding features (ct/airways/lungs) |
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for d in dataset["test"]: |
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print(d) |
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``` |
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A detailed interactive demo on how to load and work with the data can be seen on CoLab. Click the CoLab badge <a href="https://colab.research.google.com/gist/andreped/6070d1d2914a9ce5847d4b3e687188b7/aeropath-load-dataset-example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> to see the notebook or alternatively click [here](https://github.com/raidionics/AeroPath/blob/main/notebooks/aeropath-load-dataset-example.ipynb) to see it on GitHub. |
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### [Dataset structure](https://github.com/raidionics/AeroPath#dataset-structure) |
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``` |
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└── AeroPath.zip |
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├── README.md |
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└── AeroPath/ |
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├── 1/ |
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│ ├── 1_CT_HR.nii.gz |
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│ ├── 1_CT_HR_label_airways.nii.gz |
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│ └── 1_CT_HR_label_lungs.nii.gz |
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├── [...] |
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└── 27/ |
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├── 27_CT_HR.nii.gz |
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├── 27_CT_HR_label_airways.nii.gz |
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└── 27_CT_HR_label_lungs.nii.gz |
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``` |
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## [Demo](https://github.com/raidionics/AeroPath#demo) <a target="_blank" href="https://huggingface.co/spaces/andreped/AeroPath"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a> |
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To access the live demo, click on the `Hugging Face` badge above. Below is a snapshot of the current state of the demo app. |
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<img width="1400" alt="Screenshot 2023-10-31 at 01 34 47" src="https://github.com/raidionics/AeroPath/assets/29090665/bd2db9ff-b188-4f90-aa96-4723b8e7597c"> |
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## [Continuous integration](https://github.com/raidionics/AeroPath#continuous-integration) |
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| Build Type | Status | |
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| - | - | |
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| **HF Deploy** | [](https://github.com/raidionics/AeroPath/actions) | |
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| **File size check** | [](https://github.com/raidionics/AeroPath/actions) | |
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| **Formatting check** | [](https://github.com/raidionics/AeroPath/actions) | |
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## [Development](https://github.com/raidionics/AeroPath#development) |
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### [Docker](https://github.com/raidionics/AeroPath#docker) |
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Alternatively, you can deploy the software locally. Note that this is only relevant for development purposes. Simply dockerize the app and run it: |
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``` |
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docker build -t AeroPath . |
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docker run -it -p 7860:7860 AeroPath |
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``` |
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Then open `http://127.0.0.1:7860` in your favourite internet browser to view the demo. |
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### [Python](https://github.com/raidionics/AeroPath#python) |
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It is also possible to run the app locally without Docker. Just setup a virtual environment and run the app. |
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Note that the current working directory would need to be adjusted based on where `AeroPath` is located on disk. |
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``` |
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git clone https://github.com/raidionics/AeroPath.git |
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cd AeroPath/ |
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virtualenv -python3 venv --clear |
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source venv/bin/activate |
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pip install -r ./demo/requirements.txt |
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python demo/app.py --cwd ./ |
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``` |
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## [Citation](https://github.com/raidionics/AeroPath#citation) |
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If you found the dataset and/or web application relevant in your research, please cite the following reference: |
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``` |
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@misc{støverud2023aeropath, |
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title={{AeroPath: An airway segmentation benchmark dataset with challenging pathology}}, |
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author={Karen-Helene Støverud and David Bouget and Andre Pedersen and Håkon Olav Leira and Thomas Langø and Erlend Fagertun Hofstad}, |
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year={2023}, |
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eprint={2311.01138}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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The dataset is hosted at Zenodo, so you should also cite the following: |
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``` |
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@dataset{hofstad2023aeropathzenodo, |
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title = {{AeroPath: An airway segmentation benchmark dataset with challenging pathology}}, |
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author = {Hofstad, Erlend and Bouget, David and Pedersen, André}, |
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month = nov, |
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year = 2023, |
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publisher = {Zenodo}, |
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doi = {10.5281/zenodo.10069289}, |
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url = {https://doi.org/10.5281/zenodo.10069289} |
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} |
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``` |
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The web application is using the [Raidionics]() backend, thus, also consider citing: |
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``` |
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@article{bouget2023raidionics, |
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title = {Raidionics: an open software for pre-and postoperative central nervous system tumor segmentation and standardized reporting}, |
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author = {Bouget, David and Alsinan, Demah and Gaitan, Valeria and Holden Helland, Ragnhild and Pedersen, André and Solheim, Ole and Reinertsen, Ingerid}, |
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year = {2023}, |
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month = {09}, |
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pages = {}, |
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volume = {13}, |
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journal = {Scientific Reports}, |
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doi = {10.1038/s41598-023-42048-7}, |
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} |
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``` |
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## [License](https://github.com/raidionics/AeroPath#license) |
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The code in this repository is released under [MIT license](https://github.com/raidionics/AeroPath/blob/main/LICENSE.md). |
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