To perform a fully-automated segmentation of cardiac volumes, current Convolutional Neural Networks (CNNs) process each slice independently, not taking the depth information into consideration. Networks using 3D convolutions being memory-hungry, we propose a CNN model with a low memory demand and processing the whole volume. The network is based on propagating the redundant depth information from slice to slice. Following a 4-fold cross validation on the MICCAI/ACDC challenge dataset, our network obtained better results than a standard 2D network, improving the average DICE score of 1.7% computed over three cardiac structures (myocardium, left and right ventricle).
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