Coronary artery calcification (CAC) is a strong and independent predictor of
cardiovascular disease (CVD). However, manual assessment of CAC often requires
radiological expertise, time, and invasive imaging techniques. The purpose of
this multicenter study is to validate an automated cardiac plaque detection
model using a 3D multiclass nnU-Net for gated and non-gated non-contrast chest
CT volumes. CT scans were performed at three tertiary care hospitals and
collected as three datasets, respectively. Heart, aorta, and lung segmentations
were determined using TotalSegmentator, while plaques in the coronary arteries
and heart valves were manually labeled for 801 volumes. In this work we
demonstrate how the nnU-Net semantic segmentation pipeline may be adapted to
detect plaques in the coronary arteries and valves. With a linear correction,
nnU-Net deep learning methods may also accurately estimate Agatston scores on
chest non-contrast CT scans. Compared to manual Agatson scoring, automated
Agatston scoring indicated a slope of the linear regression of 0.841 with an
intercept of +16 HU (R2 = 0.97). These results are an improvement over previous
work assessing automated Agatston score computation in non-gated CT scans.

本多中心研究旨在使用 3D 多类别 nnU-Net 验证一种自动化心脏斑块检测模型，以用于门控和非门控的非对比度胸部 CT 体积，证明了 nnU-Net 语义分割管道如何适应于检测冠状动脉和瓣膜斑块，并展示了使用线性修正时，nnU-Net 深度学习方法在胸部非对比度 CT 扫描中准确估计 Agatston 分数的能力，相较于手动的 Agatson 评分，在非门控 CT 扫描中，自动化 Agatston 评分显示了线性回归斜率为 0.841 和截距为 + 16 HU（R2 = 0.97），这些结果比先前检测自动化 Agatston 评分的工作有所改进。