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
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: MIDL 2019 [arXiv:1907.08612]
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.02994 Zobrazit plný text záznamu View this record from Arxiv