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

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

Apr 10, 2019 MIDL 2019 Conference Abstract Submission readers: everyone Show Bibtex
  • Keywords: Cardiac segmentation, deep learning, ultrasound, left ventricle, myocardium
  • TL;DR: We propose in this abstract an extension of the evaluation criteria of our recently submitted study to anatomical assessment.
  • 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 overall plausibility of the predicted shapes. The completed study sheds a new light on the ranking of models.
  • Code Of Conduct: I have read and accept the code of conduct.
  • Remove If Rejected: Remove submission from public view if paper is rejected.
  • Link: https://ieeexplore.ieee.org/document/8649738
0 Replies

Loading