发表机构
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种分层建模框架，将SYNTAX评分重构为保留血管段身份的解剖推理问题，整合公共数据集，通过段级狭窄嵌入提升可解释性并降低预测变异性，R^2提升0.201。
AI 中文摘要
SYNTAX评分是一种临床公认的工具，用于评估冠状动脉疾病中解剖病变的复杂性并指导后续治疗。然而，自动化的SYNTAX评分通常被表述为从冠状动脉造影视频到患者水平评分的直接回归问题。在这项工作中，我们将SYNTAX评分重新表述为一个保留血管段身份的解剖推理问题，并提出了一个分层建模框架，该框架明确地将学习与临床工作流对齐。我们的方法在帧和视图之间保持血管段身份，在段水平估计狭窄严重程度，并根据冠状动脉解剖结构分层聚合证据。同时，为了解决特定领域数据稀缺的问题，我们整合并完善了多个公共冠状动脉造影数据集，构建了一个具有完整血管分割和派生结构注释的大规模资源。实验表明，与基线模型相比，血管段水平狭窄嵌入增强了可解释性并减少了预测变异性，R^2分数提高了0.201，偏差标准差降低了18.4%。这些结果突显了结构对齐建模对于从多视图冠状动脉造影视频中进行可靠且稳定的自动化SYNTAX评分的必要性。GitHub链接为https URL。
英文摘要
The SYNTAX score is a clinically established tool for assessing anatomical lesion complexity in coronary artery disease and guiding subsequent treatment. However, automated SYNTAX scoring is commonly formulated as a direct regression problem from coronary angiography videos to patient-level scores. In this work, we reformulate SYNTAX scoring as a vessel segment identity-preserving anatomical reasoning problem and propose a hierarchical modeling framework that explicitly aligns learning with the clinical workflow. Our approach maintains vessel segment identity across frames and views, estimates stenosis severity at the segment level, and aggregates evidence hierarchically according to coronary anatomy. Simultaneously, to address the scarcity of domain-specific data, we integrate and complete multiple public coronary angiography datasets, constructing a large-scale resource featuring completed vessel segmentation and derived structural annotations. Experiments demonstrate that vessel segment-level stenosis embedding enhances explanatory power and reduces prediction variability compared to baseline models, with the R^2 score improving by 0.201 and dev STD decreasing by 18.4%. These results highlight the necessity of structure-aligned modeling for reliable and stable automated SYNTAX scoring from multi-view coronary angiography videos. The GitHub link is https://github.com/VersaceSu7/SYNTAX_score_777.
