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.

翻译：X射线冠状动脉血管造影（XCA）是评估冠状动脉疾病的临床金标准，但由于常规数据中血管分割的鲁棒性难题，其定量分析受到限制。低对比度、运动伪影、透视缩短、血管重叠以及导管干扰等因素会降低分割质量，并导致不同医疗中心之间的域偏移。可靠的分割结合血管类型标注，能够实现基于解剖定位的血管特异性冠脉分析与下游测量。我们从670个动态序列（407名受试者）中，采用低强度直方图准则选取近峰值显影的最佳帧，并应用联合超分辨率与增强技术。我们在三种配置下对经典血管滤波器（Meijering、Frangi、Sato）进行基准测试：每帧图像的最优参数调优、单一全局均值设置，以及通过支持向量回归（SVR）实现的每帧参数预测。神经网络基线包括U-Net、FPN和Swin Transformer，分别使用纯冠脉标注及冠脉+导管合并标注进行训练。第二阶段进行血管类型识别（左前降支LAD、左回旋支LCX、右冠状动脉RCA）。外部评估采用公开数据集DCA1队列。相较于全局均值设置，SVR每帧调优策略将所有经典滤波器的Dice系数均提升（例如Frangi：0.759 vs. 0.741）。在深度学习模型中，FPN在纯冠脉标注下获得0.914+/-0.007的Dice系数，而冠脉+导管合并标注进一步提升至0.931+/-0.006。在严格的DCA1外部测试中，Dice系数降至0.798（纯冠脉）和0.814（合并标注），而轻量级域内微调可恢复至0.881+/-0.014和0.882+/-0.015。血管类型标注对RCA、LAD、LCX的识别准确率分别达到98.5%（Dice 0.844）、95.4%（0.786）和96.2%（0.794）。研究表明：基于学习的每帧参数调优能增强经典流程的性能，而高分辨率FPN模型与合并标注监督策略通过适度适配即可提升稳定性和外部泛化能力。