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title: 'AeroPath: automatic airway segmentation using deep learning' | |
colorFrom: indigo | |
colorTo: indigo | |
sdk: docker | |
app_port: 7860 | |
emoji: π« | |
pinned: false | |
license: mit | |
app_file: demo/app.py | |
<div align="center"> | |
<h1 align="center">π« AeroPath π€</h1> | |
<h3 align="center">An airway segmentation benchmark dataset with challenging pathology</h3> | |
[![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/raidionics/AeroPath/blob/main/LICENSE.md) | |
[![CI/CD](https://github.com/raidionics/AeroPath/actions/workflows/deploy.yml/badge.svg)](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/π€%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> | |
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10069288.svg)](https://doi.org/10.5281/zenodo.10069288) | |
[![paper](https://img.shields.io/badge/arXiv-preprint-D12424)](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) | |
``` | |
βββ AeroPath.zip | |
βββ README.md | |
βββ AeroPath/ | |
βββ 1/ | |
β βββ 1_CT_HR.nii.gz | |
β βββ 1_CT_HR_label_airways.nii.gz | |
β βββ 1_CT_HR_label_lungs.nii.gz | |
βββ [...] | |
βββ 27/ | |
βββ 27_CT_HR.nii.gz | |
βββ 27_CT_HR_label_airways.nii.gz | |
βββ 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/π€%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** | [![Deploy](https://github.com/raidionics/AeroPath/workflows/Deploy/badge.svg)](https://github.com/raidionics/AeroPath/actions) | | |
| **File size check** | [![Filesize](https://github.com/raidionics/AeroPath/workflows/Check%20file%20size/badge.svg)](https://github.com/raidionics/AeroPath/actions) | | |
| **Formatting check** | [![Filesize](https://github.com/raidionics/AeroPath/workflows/Linting/badge.svg)](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{stΓΈverud2023aeropath, | |
title={{AeroPath: An airway segmentation benchmark dataset with challenging pathology}}, | |
author={Karen-Helene StΓΈverud and David Bouget and Andre Pedersen and HΓ₯kon Olav Leira and Thomas LangΓΈ 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, AndrΓ©}, | |
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, AndrΓ© 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). | |