标题： LWT-ARTERY-LABEL：用于冠状动脉自动识别的轻量级框架 显示英文标题

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

摘要： 冠状动脉疾病（CAD）仍然是全球死亡的主要原因，计算机断层扫描冠状动脉造影（CTCA）作为关键的诊断工具。 然而，使用CTCA进行冠状动脉分析，如从计算模型中识别动脉特异性特征，既耗时又费力。 自动化冠状动脉解剖标记提供了一个潜在的解决方案，但冠状动脉树的固有解剖变异性带来了重大挑战。 传统的基于知识的标记方法在利用数据驱动的见解方面存在不足，而最近的深度学习方法通常需要大量的计算资源，并忽视了关键的临床知识。 为了解决这些限制，我们提出了一种轻量级的方法，将解剖学知识与基于规则的拓扑约束相结合，以实现有效的冠状动脉标记。 我们的方法在基准数据集上达到了最先进的性能，为自动冠状动脉标记提供了一个有前景的替代方案。 显示英文摘要

摘要： 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.