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Quantification of cardiac structures in non-contrast CT (NCCT) could improvecardiovascular risk stratification. However, setting a manual reference totrain a fully convolutional network (FCN) for automatic segmentation of NCCTimages is hardly feasible, and an FCN trained on coronary CT angiography (CCTA)images would not generalize to NCCT. Therefore, we propose to train an FCN withvirtual non-contrast (VNC) images from a dual-layer detector CT scanner and areference standard obtained on perfectly aligned CCTA images.

CNN-Based Segmentation of the Cardiac Chambers and Great Vessels in Non-Contrast-Enhanced Cardiac CT