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

Coronary artery disease (CAD) remains the leading cause of death globally, and Computed Tomography Coronary Angiography (CTCA) images are commonly used to decide on the final treatment route. CTCA contain a wealth of information which gives rise to an emerging effort of further analysis via computational modelling. However, a bottleneck in this pursuit is the identification of artery-specific features which 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.Clinical RelevanceWe introduce an automated artery labelling method for efficient coronary arterial analysis, such as identifying artery-specific features for further computational analysis.