发表机构
DTU Compute, Technical University of Denmark; Cardiovascular Research Unit, Copenhagen University Hospital – Rigshospitalet（丹麦技术大学DTU计算学院; 哥本哈根大学医院——国立医院心血管研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建了含800例扫描标注的ImageCAS-X数据集，对冠状动脉CT血管造影的管腔分割方法开展多维度基准测试，为相关医学影像分析方法的开发验证提供支撑。
AI 中文摘要
冠状动脉管腔的准确分割是冠状动脉CT血管造影（CCTA）中定量评估动脉粥样硬化斑块和血管周围脂肪组织的前提条件。由于手动血管追踪与分割耗时费力，心脏病学家依赖半自动方法完成该任务。尽管已提出多种自动化方法，但因缺乏大型、高质量公开可用数据集，其验证仍受限。我们从公开的ImageCAS数据集中提供800例扫描的管腔、冠状动脉节段的体素级标注，以及中心线和网格表面的新数据集。利用该数据集，我们将已建立的管腔分割方法与观察者间变异性进行基准测试，按疾病、图像质量、冠状动脉优势型、冠状动脉节段、血管直径和管腔衰减对性能进行分层。这些标注使分割准确率可在解剖学和临床背景下描述，而非作为单一汇总分数报告。该数据集支持管腔分割、斑块与血管周围量化及血流动力学建模方法的开发与验证。
英文摘要
Accurate segmentation of the coronary vessel lumen is a prerequisite for quantitative assessment of atherosclerotic plaque and perivascular adipose tissue in coronary computed tomography angiography (CCTA). Cardiologists rely on semi-automated methods for this task because manual vessel tracing and segmentation are labour-intensive. Although many automated methods have been proposed, their validation remains limited by the lack of large, high-quality publicly available datasets. We provide a new dataset of voxel-wise annotations of the vessel lumen and coronary segments, alongside centerlines, and mesh surfaces for 800 scans from the publicly available ImageCAS dataset. Using this dataset, we benchmark established lumen segmentation methods against inter-observer variability, stratifying performance by disease, image quality, coronary dominance, coronary segment, vessel diameter, and lumen attenuation. These labels allow segmentation accuracy to be described in anatomical and clinical context rather than reported as a single aggregate score. The dataset supports the development and validation of methods for lumen segmentation, plaque and perivascular quantification, and haemodynamic modelling.
CommentsPre-print (under review)
