We present DeepVentricle, an automated approach to ventricular segmentation in cardiac MR. DeepVentricle uses a fully convolutional neural network to simultaneously perform semantic segmentation of the left ventricle (LV) and right ventricle (RV) endocardium, and LV epicardium; segmentations are then used to estimate ejection fraction and myocardial mass. We show that the error rates of LV ejection fraction and mass are within the expected range of expert annotator inter-rater variation. This suggests that contours calculated using DeepVentricle could be useful on their own or as an initial estimate for clinicians as part of their semi-automated annotation workflow.
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