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| 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 | |
| <div align="center"> | |
| <h1 align="center">🫁 AeroPath 🤗</h1> | |
| <h3 align="center">An airway segmentation benchmark dataset with challenging pathology</h3> | |
| [](https://github.com/raidionics/AeroPath/blob/main/LICENSE.md) | |
| [](https://github.com/raidionics/AeroPath/actions/workflows/deploy.yml) | |
| <a target="_blank" href="https://huggingface.co/spaces/andreped/AeroPath"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a> | |
| <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> | |
| [](https://doi.org/10.5281/zenodo.10069288) | |
| [](https://arxiv.org/abs/2311.01138) | |
| **AeroPath** was developed by SINTEF Medical Image Analysis to accelerate medical AI research. | |
| </div> | |
| ## [Brief intro](https://github.com/raidionics/AeroPath#brief-intro) | |
| 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. | |
| 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. | |
| ## [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> | |
| ### [Accessing dataset](https://github.com/raidionics/AeroPath#accessing-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](https://pypi.org/project/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 <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. | |
| ### [Dataset structure](https://github.com/raidionics/AeroPath#dataset-structure) | |
| ``` | |
| └── 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 | |
| ``` | |
| ## [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> | |
| To access the live demo, click on the `Hugging Face` badge above. Below is a snapshot of the current state of the demo app. | |
| <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"> | |
| ## [Continuous integration](https://github.com/raidionics/AeroPath#continuous-integration) | |
| | Build Type | Status | | |
| | - | - | | |
| | **HF Deploy** | [](https://github.com/raidionics/AeroPath/actions) | | |
| | **File size check** | [](https://github.com/raidionics/AeroPath/actions) | | |
| | **Formatting check** | [](https://github.com/raidionics/AeroPath/actions) | | |
| ## [Development](https://github.com/raidionics/AeroPath#development) | |
| ### [Docker](https://github.com/raidionics/AeroPath#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](https://github.com/raidionics/AeroPath#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](https://github.com/raidionics/AeroPath#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 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}, | |
| } | |
| ``` | |
| ## [License](https://github.com/raidionics/AeroPath#license) | |
| The code in this repository is released under [MIT license](https://github.com/raidionics/AeroPath/blob/main/LICENSE.md). | |