LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification
This work addresses the need for efficient and accurate coronary artery identification in clinical diagnostics for patients with coronary artery disease, though it appears incremental as it builds on existing methods by combining knowledge and constraints.
The paper tackles the problem of automated anatomical labeling of coronary arteries from CTCA images, which is labor-intensive and challenging due to anatomical variability, by proposing a lightweight method that integrates anatomical knowledge with rule-based topology constraints, achieving state-of-the-art performance on benchmark datasets.
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.