Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 9, 2018

Abstract

PURPOSE: The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.

Authors

  • Michał Byra
    Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106, Warsaw, Poland. mbyra@ippt.pan.pl.
  • Grzegorz Styczynski
    Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Warsaw, Poland.
  • Cezary Szmigielski
    Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Warsaw, Poland.
  • Piotr Kalinowski
    Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Warsaw, Poland.
  • Łukasz Michałowski
    Department of Pathology, Center for Biostructure Research, Medical University of Warsaw, Warsaw, Poland.
  • Rafał Paluszkiewicz
    Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Warsaw, Poland.
  • Bogna Ziarkiewicz-Wróblewska
    Department of Pathology, Center for Biostructure Research, Medical University of Warsaw, Warsaw, Poland.
  • Krzysztof Zieniewicz
    Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Warsaw, Poland.
  • Piotr Sobieraj
    Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Warsaw, Poland.
  • Andrzej Nowicki
    Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106, Warsaw, Poland.

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