轻量级框架自动标注冠状动脉，提升诊断效率
LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification
- 融合解剖知识与规则拓扑约束实现自动化标注
- 在基准数据集上达到顶尖性能，准确率显著提升
- 适合临床医生快速辅助诊断，兼顾精度与效率
冠心病（CAD）是全球首要致死原因，计算机断层扫描冠状动脉造影（CTCA）是关键诊断工具。然而，基于CTCA的冠状动脉分析（如计算建模中的血管特异性特征识别）仍依赖大量人工，耗时费力。自动解剖标注可缓解此问题，但冠状动脉树的固有解剖变异性带来挑战。传统基于知识的方法难以挖掘数据驱动洞见，而近期深度学习方法常需大量算力且忽视临床知识。为此，我们提出一种轻量级方法，将解剖知识与规则化拓扑约束结合，实现高效的冠状动脉标注。该方法在基准数据集上达到当前最优性能，为自动化冠状动脉标注提供可行方案。
原文摘要 · Abstract (English)
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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