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

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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https://hal.archives-ouvertes.fr/hal-02395245
Contributor : Carole Lartizien <>
Submitted on : Thursday, December 5, 2019 - 12:47:08 PM
Last modification on : Friday, September 4, 2020 - 3:38:02 PM
Long-term archiving on: : Friday, March 6, 2020 - 4:47:49 PM

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  • HAL Id : hal-02395245, version 1

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Sarah Leclerc, Erik Smistad, Andreas Ostvik, Frederic Cervenansky, Florian Espinosa, et al.. Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity. International conference on Medical Imaging with Deep Learning (MIDL 2019), Jul 2019, London, United Kingdom. ⟨hal-02395245⟩

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