首个公开的左心房附壁段高精度分割数据集，助力心脏影像分析。
A public cardiac CT dataset featuring the left atrial appendage
- 基于1000例CCTA扫描构建，融合人工标注与自动分割优化
- 实现左心房附壁段、冠状动脉、肺静脉的精准标注，提升形态分析能力
- 适用于心脏病研究、模型训练及数据质量评估，适合医学影像开发者
尽管如TotalSegmentator（TS）等先进分割框架取得成功，左心房附壁段（LAA）、冠状动脉（CAs）和肺静脉（PVs）的精确分割仍是医学影像中的重大挑战。本文首次发布一个开源、解剖一致的高质量标注数据集，包含1000例心脏CT血管造影（CCTA）扫描的精细分割结果。该数据集基于公开的ImageCAS数据集构建，其中全心脏标签由TS生成，而LAA分割采用专为高分辨率设计的先进分割框架，该模型在大型私有数据集上训练并经心脏病专家指导完成标注后迁移至ImageCAS。CAs标签经改进，PV分割则由TS输出进一步精修。此外，我们还列出包含常见数据缺陷（如层间伪影、LAA超出视野、其他数据问题）的扫描编号，便于后续质量控制。
原文摘要 · Abstract (English)
Despite the success of advanced segmentation frameworks such as TotalSegmentator (TS), accurate segmentations of the left atrial appendage (LAA), coronary arteries (CAs), and pulmonary veins (PVs) remain a significant challenge in medical imaging. In this work, we present the first open-source, anatomically coherent dataset of curated, high-resolution segmentations for these structures, supplemented with whole-heart labels produced by TS on the publicly available ImageCAS dataset consisting of 1000 cardiac computed tomography angiography (CCTA) scans. One purpose of the data set is to foster novel approaches to the analysis of LAA morphology. LAA segmentations on ImageCAS were generated using a state-of-the-art segmentation framework developed specifically for high resolution LAA segmentation. We trained the network on a large private dataset with manual annotations provided by medical readers guided by a trained cardiologist and transferred the model to ImageCAS data. CA labels were improved from the original ImageCAS annotations, while PV segmentations were refined from TS outputs. In addition, we provide a list of scans from ImageCAS that contains common data flaws such as step artefacts, LAAs extending beyond the scanner's field of view, and other types of data defects.
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