Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
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 \(131\times 131\) 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.
KeywordsDeep learning Dilated convolutional neural networks Medical image segmentation Cardiovascular MR Congenital heart disease
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
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