Výsledky vyhledávání - "Cervenansky, Frederic"

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LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography

Autor: Leclerc, Sarah, Smistad, Erik, Østvik, Andreas, Cervenansky, Frederic, Espinosa, Florian, Espeland, Torvald, Berg, Erik Andreas Rye, Grenier, Thomas, Lartizien, Carole, Jodoin, Pierre-Marc, Lovstakken, Lasse, Bernard, Olivier

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability. Recent st

Externí odkaz: http://arxiv.org/abs/2004.02043

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Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography

Autor: Leclerc, Sarah, Smistad, Erik, Pedrosa, João, Østvik, Andreas, Cervenansky, Frederic, Espinosa, Florian, Espeland, Torvald, Berg, Erik Andreas Rye, Jodoin, Pierre-Marc, Grenier, Thomas, Lartizien, Carole, D'hooge, Jan, Lovstakken, Lasse, Bernard, Olivier

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far

Externí odkaz: http://arxiv.org/abs/1908.06948

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Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity

Autor: Leclerc, Sarah, Smistad, Erik, Østvik, Andreas, Cervenansky, Frederic, Espinosa, Florian, Espeland, Torvald, Berg, Erik Andreas Rye, Jodoin, Pierre-Marc, Grenier, Thomas, Lartizien, Carole, Lovstakken, Lasse, Bernard, Olivier

We recently published a deep learning study on the potential of encoder-decoder networks for the segmentation of the 2D CAMUS ultrasound dataset. We propose in this abstract an extension of the evaluation criteria to anatomical assessment, as traditi

Externí odkaz: http://arxiv.org/abs/1908.02994

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Boutiques: a flexible framework for automated application integration in computing platforms

We present Boutiques, a system to automatically publish, integrate and execute applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core

Externí odkaz: http://arxiv.org/abs/1711.09713

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