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.

翻译：冠状动脉钙化（CAC）是心血管疾病（CVD）的强效独立预测因子。然而，人工评估CAC通常需要放射学专业知识、时间及侵入性成像技术。本项多中心研究旨在验证一种基于三维多类别nnU-Net的自动心脏斑块检测模型，该模型适用于门控与非门控非对比胸部CT体积数据。CT扫描在三家三级医院进行，并分别收集为三个数据集。通过TotalSegmentator确定心脏、主动脉和肺部分割结果，而冠状动脉及心脏瓣膜中的斑块则由人工标注于801个体积中。本研究展示了如何将nnU-Net语义分割流程适配为检测冠状动脉及瓣膜中的斑块。通过线性校正，nnU-Net深度学习方法还可准确估算胸部非对比CT扫描中的Agatston评分。相较于人工Agatston评分，自动Agatston评分的线性回归斜率为0.841，截距为+16 HU（R² = 0.97）。这些结果较先前评估非门控CT扫描中自动Agatston评分计算的研究有显著改进。