Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.

Journal: Medical physics
Published Date: May 1, 2019

Abstract

PURPOSE: To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs).

Authors

  • Ilias Gatos
    Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece.
  • Stavros Tsantis
    Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece.
  • Stavros Spiliopoulos
    Department of Radiology, School of Medicine, University of Patras, Rion GR 26504, Greece.
  • Dimitris Karnabatidis
    Department of Radiology, School of Medicine, University of Patras, Patras, Greece.
  • Ioannis Theotokas
    Diagnostic Echotomography SA, 317C Kifissias Avenue, Kifissia GR 14561, Greece.
  • Pavlos Zoumpoulis
    Diagnostic Echotomography SA, 317C Kifissias Avenue, Kifissia GR 14561, Greece.
  • Thanasis Loupas
    SuperSonic Imagine SA, Aix-en-Provence, France.
  • John D Hazle
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
  • George C Kagadis
    Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece and Department of Imaging Physics, The University of  Texas MD Anderson Cancer Center, Houston, Texas 77030.

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