  Coronary artery calcium (CAC) burden quantified in low-dose chest CT is a
predictor of cardiovascular events. We propose an automatic method for CAC
quantification, circumventing intermediate segmentation of CAC. The method
determines a bounding box around the heart using a ConvNet for localization.
Subsequently, a dedicated ConvNet analyzes axial slices within the bounding
boxes to determine CAC quantity by regression. A dataset of 1,546 baseline CT
scans was used from the National Lung Screening Trial with manually identified
CAC. The method achieved an ICC of 0.98 between manual reference and
automatically obtained Agatston scores. Stratification of subjects into five
cardiovascular risk categories resulted in an accuracy of 85\% and Cohen's
linearly weighted $\kappa$ of 0.90. The results demonstrate that real-time
quantification of CAC burden in chest CT without the need for segmentation of
CAC is possible.
