  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.
