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

TL;DR

This paper introduces a lightweight, data-driven framework that combines anatomical knowledge and rule-based constraints to automate coronary artery labelling, addressing variability and resource demands in clinical imaging.

Contribution

The proposed method integrates anatomical knowledge with topology constraints, offering a resource-efficient alternative to existing deep-learning approaches for coronary artery labelling.

Findings

Achieves state-of-the-art accuracy on benchmark datasets.

Reduces computational resource requirements compared to deep-learning methods.

Effectively handles anatomical variability in coronary trees.

Abstract

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.