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arXiv · 1908.02994

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

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

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 traditional geometric and clinical metrics in cardiac segmentation do not take into account the anatomical correctness of the predicted shapes. The completed study sheds a new light on the ranking of models.

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Sarah Leclerc, Erik Smistad, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Pierre-Marc Jodoin, Thomas Grenier, Carole Lartizien, Lasse Lovstakken, Olivier Bernard. 2019-08-08. Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity. https://arxiv.org/abs/1908.02994

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