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arXiv · 2508.06874

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

Shisheng Zhang·Ramtin Gharleghi·Sonit Singh·Daniel Moses·Dona Adikari·Arcot Sowmya·Susann Beier
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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.

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Shisheng Zhang, Ramtin Gharleghi, Sonit Singh, Daniel Moses, Dona Adikari, Arcot Sowmya, Susann Beier. 2025-08-09. LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification. https://arxiv.org/abs/2508.06874

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