基于NLST数据集，自动生成冠脉钙化评分与风险分层，助力肺部CT的潜在心血管评估。
Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2)
- 利用深度学习自动分割低剂量胸部CT中的冠脉钙化区域
- 处理26,228人共127,776张扫描图像，生成标准化钙化评分和风险类别
- 数据集与代码开源，支持可复现的心血管风险研究
冠状动脉钙化（CAC）是心血管风险的重要预测因子，但因需专用成像协议和人工标注，在常规胸部影像中未被广泛应用。本文提出DeepCAC2，一个公开数据集，包含从国家肺癌筛查试验（NLST）低剂量胸部CT扫描中自动生成的冠脉钙化分割结果、钙化积分及衍生风险分类。采用在专家标注心脏CT数据上训练的全自动深度学习流程，对26,228名受试者共127,776张扫描图像进行了处理，为每例扫描生成标准化的CAC分割与风险评估。目前已提供面向200名随机患者的数据可视化仪表板。数据集将以兼容DICOM的分割对象和结构化元数据形式发布，支持可复现的后续分析。深度学习流程将作为兼容DICOM的MHub.ai容器公开。DeepCAC2为心血管风险评估、机会性筛查和影像生物标志物开发提供了透明、大规模、公开且完全可复现的研究资源。
原文摘要 · Abstract (English)
Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imaging protocols and manual annotation. We present DeepCAC2, a publicly available dataset containing automated CAC segmentations, coronary artery calcium scores, and derived risk categories generated from low-dose chest CT scans of the National Lung Screening Trial (NLST). Using a fully automated deep learning pipeline trained on expert-annotated cardiac CT data, we processed 127,776 CT scans from 26,228 individuals and generated standardized CAC segmentations and risk estimates for each acquisition. We already provide a public dashboard as a simple tool to visually inspect a random subset of 200 NLST patients of the dataset. The dataset will be released with DICOM-compatible segmentation objects and structured metadata to support reproducible downstream analysis. The deep learning pipeline will be made publicly available as a DICOM-compatible MHub.ai container. DeepCAC2 provides a transparent, large-scale, public, fully reproducible resource for research in cardiovascular risk assessment, opportunistic screening, and imaging biomarker development.
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