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RU-Net: A refining segmentation network for 2D echocardiography

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  • معلومة اضافية
    • Contributors:
      Modeling & analysis for medical imaging and Diagnosis (MYRIAD); Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS); Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS); Norwegian University of Science and Technology Trondheim (NTNU); Norwegian University of Science and Technology (NTNU); Service Informatique et développements; Centre Hospitalier Universitaire de Saint-Etienne CHU Saint-Etienne (CHU ST-E); Université de Sherbrooke (UdeS); Department of Circulation and Medical Imaging Trondheim (ISB NTNU); Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU)
    • بيانات النشر:
      HAL CCSD
      IEEE
    • الموضوع:
      2019
    • Collection:
      Université de Lyon: HAL
    • الموضوع:
      Glasgow; France
    • نبذة مختصرة :
      International audience ; In this work, we present a novel attention mechanism to refine the segmentation of the endocardium and epicardium in 2D echocardiography. A combination of two U-Nets is used to derive a region of interest in the image before the segmentation. By relying on parameterised sigmoids to perform thresholding operations, the full pipeline is trainable end-to-end. The Refining U-Net (RU-Net) architecture is evaluated on the CAMUS dataset, comprising 2000 annotated images from the apical 2 and 4 chamber views of 500 patients. Although geometrical scores are only marginally improved, the reduction in outlier predictions (from 20% to 16%) supports the interest of such approach.
    • Relation:
      hal-02570017; https://hal.science/hal-02570017; https://hal.science/hal-02570017/document; https://hal.science/hal-02570017/file/articleIUS2019%20%282%29.pdf
    • الرقم المعرف:
      10.1109/ULTSYM.2019.8926158
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.3E76A928