  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 were
provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive
field of 131x131 voxels was trained for myocardium and blood pool segmentation
in axial, sagittal and coronal image slices. Performance was evaluated within
the HVSMR challenge.
  Automatic segmentation of the test scans resulted in Dice indices of
0.80$\pm$0.06 and 0.93$\pm$0.02, average distances to boundaries of
0.96$\pm$0.31 and 0.89$\pm$0.24 mm, and Hausdorff distances of 6.13$\pm$3.76
and 7.07$\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 CHD
patients showing large anatomical variability provide accurate myocardium and
blood pool segmentations.
