RU-Net: A refining segmentation network for 2D echocardiography
Sarah Leclerc, Pierre‐Marc Jodoin, Lasse Løvstakken, Olivier Bernard, Erik Smistad, Thomas Grenier, Carole Lartizien, Andreas Østvik, Frédéric Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg · 2019
In this work, we present a novel attention mechanism to refine the segmentation of the endocardium and epicardium in 2D echocardiography. A combination of two U-Nets is used to derive a region of interest in the image before the segmentation. By relying on parameterised sigmoids to perform thresholding operations, the full pipeline is trainable end-to-end. The Refining U-Net (RU-Net) architecture is evaluated on the CAMUS dataset, comprising 2000 annotated images from the apical 2 and 4 chamber views of 500 patients. Although geometrical scores are only marginally improved, the reduction in outlier predictions (from 20% to 16%) supports the interest of such approach.