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In this paper, we propose a fully automatic MRI cardiac segmentation methodbased on a novel deep convolutional neural network (CNN) designed for the 2017ACDC MICCAI challenge. The novelty of our network comes with its embedded shapeprior and its loss function tailored to the cardiac anatomy. Our model includesa cardiac centerof-mass regression module which allows for an automatic shapeprior registration. Also, since our method processes raw MR images without anymanual preprocessing and/or image cropping, our CNN learns both high-levelfeatures (useful to distinguish the heart from other organs with a similarshape) and low-level features (useful to get accurate segmentation results).Those features are learned with a multi-resolution conv-deconv "grid"architecture which can be seen as an extension of the U-Net. Experimentalresults reveal that our method can segment the left and right ventricles aswell as the myocardium from a 3D MRI cardiac volume in 0.4 second with anaverage Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.

GridNet with automatic shape prior registration for automatic MRI cardiac segmentation