---
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">automatic airway segmentation using deep learning</h3>

[![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/DAVFoundation/captain-n3m0/blob/master/LICENSE)
[![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/🤗%20Hugging%20Face-Spaces-yellow.svg"></a>

**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 can be accessed from [Releases](https://github.com/raidionics/AeroPath/releases).

## [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** | [![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{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 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},
}
```