提升心导管造影中冠脉分割与分型精度，助力临床量化分析
Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography
- 通过图像增强与自适应参数调优，提升经典血管检测方法的鲁棒性
- 深度模型在合并导管标签监督下，分割Dice达0.931，外部测试仍保持0.814
- 血管类型分类准确率超95%，适用于跨中心临床部署
X射线冠状动脉造影（XCA）是评估冠心病的临床金标准，但常规数据中血管分割困难限制了定量分析。低对比度、运动模糊、投影失真、重叠及导管干扰导致分割性能下降，并引发不同中心间的领域偏移。可靠分割结合血管类型标注，可实现基于解剖定位的特异性冠脉分析。基于670个心动周期序列（407例患者），采用低强度直方图准则选取造影峰值帧，实施联合超分辨率与增强处理。在单张图像最优调参、全局均值设定及支持向量回归（SVR）预测参数三种方式下，对比经典Meijering、Frangi、Sato血管响应滤波器。深度基线包括U-Net、FPN和Swin Transformer，分别使用仅冠脉与冠脉+导管联合标注进行训练。第二阶段进行血管身份识别（LAD、LCX、RCA）。外部评估采用公开的DCA1队列。结果显示，基于SVR的图像级调参使所有经典滤波器的Dice分数优于全局均值设定（如Frangi：0.759 vs. 0.741）。在深度模型中，FPN在仅冠脉监督下取得0.914±0.007的平均Dice，联合标注进一步提升至0.931±0.006。在DCA1上严格外部测试，得分降至0.798（仅冠脉）和0.814（联合），经轻量域内微调后恢复至0.881±0.014和0.882±0.015。血管类型分类准确率达98.5%（RCA, Dice 0.844）、95.4%（LAD, Dice 0.786）和96.2%（LCX, Dice 0.794）。学习式图像级调优强化经典流程，高分辨率FPN与联合标签监督则显著提升模型稳定性与跨中心泛化能力。
原文摘要 · Abstract (English)
X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.
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