# LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification

arXiv: https://arxiv.org/abs/2508.06874 · HTML: https://mlsanity.com/paper/2508.06874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries](https://mlsanity.com/paper/2003.08560/) (2020)
- [Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier](https://mlsanity.com/paper/2307.11959/) (2023)

## Abstract (arXiv)

Coronary artery disease (CAD) remains the leading cause of death globally, with computed tomography coronary angiography (CTCA) serving as a key diagnostic tool. However, coronary arterial analysis using CTCA, such as identifying artery-specific features from computational modelling, is labour-intensive and time-consuming. Automated anatomical labelling of coronary arteries offers a potential solution, yet the inherent anatomical variability of coronary trees presents a significant challenge. Traditional knowledge-based labelling methods fall short in leveraging data-driven insights, while recent deep-learning approaches often demand substantial computational resources and overlook critical clinical knowledge. To address these limitations, we propose a lightweight method that integrates anatomical knowledge with rule-based topology constraints for effective coronary artery labelling. Our approach achieves state-of-the-art performance on benchmark datasets, providing a promising alternative for automated coronary artery labelling.