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RU-Net: A refining segmentation network for 2D echocardiography

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02570017
Contributor : Sarah LECLERC Connect in order to contact the contributor
Submitted on : Monday, May 11, 2020 - 5:01:52 PM
Last modification on : Tuesday, October 18, 2022 - 4:26:57 AM

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Sarah Leclerc, Erik Smistad, Thomas Grenier, Carole Lartizien, Andreas Ostvik, et al.. RU-Net: A refining segmentation network for 2D echocardiography. 2019 IEEE International Ultrasonics Symposium (IUS), Oct 2019, Glasgow, France. pp.1160-1163, ⟨10.1109/ULTSYM.2019.8926158⟩. ⟨hal-02570017⟩

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