# YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation

arXiv: https://arxiv.org/abs/2310.15898 · HTML: https://mlsanity.com/paper/2310.15898/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Fast Guided Filter](https://mlsanity.com/paper/1505.00996/) (2015)
- [Deep Segmentation and Registration in X-Ray Angiography Video](https://mlsanity.com/paper/1805.06406/) (2018)
- [A Review of Modern Approaches for Coronary Angiography Imaging Analysis](https://mlsanity.com/paper/2209.13997/) (2022)

## Built on by (most cited first, 4 of 4)

- [Brighteye: Glaucoma Screening with Color Fundus Photographs based on Vision Transformer](https://mlsanity.com/paper/2405.00857/) (2024)
- [Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods](https://mlsanity.com/paper/2408.03393/) (2024)
- [CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography](https://mlsanity.com/paper/2607.22139/) (2026)
- [SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos](https://mlsanity.com/paper/2609.27696/) (2026)

## Abstract (arXiv)

Coronary angiography remains the gold standard for diagnosis of coronary artery disease, the most common cause of death worldwide. While this procedure is performed more than 2 million times annually, there remain few methods for fast and accurate automated measurement of disease and localization of coronary anatomy. Here, we present our solution to the Automatic Region-based Coronary Artery Disease diagnostics using X-ray angiography images (ARCADE) challenge held at MICCAI 2023. For the artery segmentation task, our three-stage approach combines preprocessing and feature selection by classical computer vision to enhance vessel contrast, followed by an ensemble model based on YOLOv8 to propose possible vessel candidates by generating a vessel map. A final segmentation is based on a logic-based approach to reconstruct the coronary tree in a graph-based sorting method. Our entry to the ARCADE challenge placed 3rd overall. Using the official metric for evaluation, we achieved an F1 score of 0.422 and 0.4289 on the validation and hold-out sets respectively.