README.md

---
title: APEC Angiography Segmentation
emoji: 🫀
colorFrom: red
colorTo: pink
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
---

# Apec Segmentation

## Apec paper
Please see [here](https://doi.org/10.1016/j.ijcard.2024.132598) for our paper in the International Journal of Cardiology

Please cite it!
> Mahendiran, T., Thanou, D., Senouf, O., Jamaa, Y., Fournier, S., De Bruyne, B., ... & Andò, E. (2025). Apec Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation. International journal of cardiology, 418, 132598.

## Apec in the news
Apec segmentation is being used in [this RTS reportage on AI in Cardiology](https://www.rts.ch/play/tv/19h30/video/lia-fait-irruption-en-cardiologie-et-redefinit-le-role-des-medecins?urn=urn:rts:video:15479233) (in French)

## Online Example
Please visit https://imaging.epfl.ch/angiopy-segmentation/ for a live demo of this code on some example DICOM images

![](illustration.mp4)

## Description
This software allows single arteries to be segmented given a few clicks on a single time frame with a PyTorch 2 Deep Learning model.

## Installing and running
 - Install dependencies: ` pip install -r requirements.txt`
 - Launch Streamlit Web Interface: `streamlit run angioPySegmentation.py --server.fileWatcherType none`

 ...a website should pop up in your browser!

 You need to create a /Dicom folder and put some angiography DICOMs in there


# How to run the project
## Create virtual environment and activate it
```bash
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt
```