Multi-class semantic segmentation and volume calculation of cardiac CT scans using convolutional neural networks

Computed tomography (CT) scans from the MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset were segmented into seven anatomical substructures (left and right ventricles, left and right atria, myocardium, aorta, and pulmonary artery) using the SpatialConfiguration-Net (SCN). The SCN utilizes a U-Net convolutional network architecture that is designed for biomedical image segmentation. The performance of the neural network was evaluated by cross validation on the training data by the Dice Similarity Coefficient over all heart substructures.