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Abstract #4729

Propagation Neural Network for cardiac segmentation

Benjamin Roussel1,2, Julien Oster2,3, and Mattias Paul Heinrich4

1Université de Lorraine, Nancy, France, 2U1254, INSERM, Nancy, France, 3Université de Lorraine, Nancy, France, Metropolitan, 4Universität zu Lübeck · Institut für Medizinische Informatik, Lübeck, Germany

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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