BSC-Net：一种用于冠状动脉血管分割和定量血管造影分析的小分支敏感结构连续性网络
BSC-Net: A Small-Branch-Sensitive Structural Continuity Network for Coronary Vessel Segmentation and Quantitative Angiographic Analysis
- Sun Yat-sen University（中山大学）
- Zhongnan Hospital of Wuhan University（武汉大学中南医院）
- West China Hospital of Sichuan University（四川大学华西医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
BSC-Net基于ResNet-U-Net，通过针对性采样和边缘信息损失增强小血管表征与结构连续性，在XCA分割中达到SOTA性能，支持定量冠状动脉分析。
AI中文摘要：
在X射线冠状动脉造影（XCA）中，血管分割是定量冠状动脉分析及后续冠状动脉疾病评估的基础步骤。然而，由于成像噪声、复杂分叉以及血管与背景结构重叠等因素，准确的血管分割仍具挑战性，这些因素可能导致血管连通性中断和小分支遗漏。在本工作中，我们提出了BSC-Net，一种基于ResNet-U-Net的框架，旨在改善小血管表征并修复血管结构连续性。BSC-Net通过针对性采样增强小血管表征，并通过整合长距离上下文建模和边缘信息损失（EIL）来改善血管结构连续性。BSC-Net在两个公开XCA数据集上进行了验证，在冠状动脉血管分割中展现了最先进的（SOTA）性能，Dice和IoU分数分别为77.8%/90.6%和64.5%/83.0%。此外，基于获得的血管分割结果，我们进行了自动化定量冠状动脉分析，并推导出临床相关的形态学和血流动力学参数，包括狭窄率、达峰时间和相对传播速度。这些结果表明，BSC-Net能够产生保留血管连续性的准确血管分割结果，用于定量冠状动脉评估，从而支持可靠的后续分析和冠状动脉疾病的临床评估。
英文摘要：
Vessel segmentation in X-ray coronary angiography (XCA) is a fundamental step for quantitative coronary analysis and subsequent assessment of coronary artery disease. However, accurate vessel segmentation remains challenging because of imaging noise, complex bifurcations, and the overlap of vessels and background structures, which can lead to disrupted vascular connectivity and missed small branches. In this work, we propose BSC-Net, a ResNet-U-Net-based framework tailored to improve small-vessel representation and repair vascular structural continuity. BSC-Net enhances small-vessel representation through targeted sampling and improves vascular structural continuity by integrating long-range contextual modeling and Edge-Informed Loss (EIL). BSC-Net was validated on two public XCA datasets, demonstrating state-of-the-art (SOTA) performance in coronary vessel segmentation with Dice and IoU scores of 77.8%/90.6% and 64.5%/83.0%, respectively. Furthermore, based on the obtained vessel segmentation, we performed automated quantitative coronary analysis and derived clinically relevant morphological and hemodynamic parameters, including stenosis ratio, time-to-peak, and relative propagation velocity. These results demonstrate that BSC-Net produces accurate vessel segmentation results with preserved vascular continuity for quantitative coronary assessment, enabling reliable downstream analysis and clinical evaluation of coronary artery disease.
