构建800例冠脉CTA标注数据集，支持精准血管分割与临床分析。
ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography
- 基于ImageCAS数据集构建800例冠脉血管腔及中心线的体素级标注。
- 首次在疾病分层、图像质量等条件下量化多种分割方法性能差异。
- 适合开发冠脉斑块与血流动力学建模的算法研究者使用。
准确分割冠状动脉管腔是定量评估冠状动脉计算机断层扫描血管造影（CCTA）中动脉粥样硬化斑块和血管周围脂肪组织的前提。由于手动追踪与分割耗时费力，心脏病学家依赖半自动化方法完成该任务。尽管已有诸多自动化方法提出，但其验证受限于缺乏大规模、高质量的公开数据集。本文提供了来自公开的ImageCAS数据集的800例扫描数据，包含体素级血管腔、冠脉节段、中心线及网格表面标注。利用该数据集，我们对现有管腔分割方法进行基准测试，按疾病状态、图像质量、冠脉优势性、冠脉节段、血管直径及管腔衰减分层评估性能。这些标注使分割精度可结合解剖与临床背景描述，而非仅报告单一综合评分。该数据集支持管腔分割、斑块与血管周围脂肪定量，以及血流动力学建模方法的开发与验证。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。