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We propose an automatic method using dilated convolutional neural networks(CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR(CMR) of patients with congenital heart disease (CHD).  Ten training and ten test CMR scans cropped to an ROI around the heart wereprovided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptivefield of 131x131 voxels was trained for myocardium and blood pool segmentationin axial, sagittal and coronal image slices. Performance was evaluated withinthe HVSMR challenge.  Automatic segmentation of the test scans resulted in Dice indices of0.80$\pm$0.06 and 0.93$\pm$0.02, average distances to boundaries of0.96$\pm$0.31 and 0.89$\pm$0.24 mm, and Hausdorff distances of 6.13$\pm$3.76and 7.07$\pm$3.01 mm for the myocardium and blood pool, respectively.Segmentation took 41.5$\pm$14.7 s per scan.  In conclusion, dilated CNNs trained on a small set of CMR images of CHDpatients showing large anatomical variability provide accurate myocardium andblood pool segmentations.

Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease