Deep Learning-based Quantification of Abdominal Subcutaneous and Visceral Fat Volume on CT Images.

Journal: Academic radiology
Published Date: Aug 6, 2020

Abstract

RATIONALE AND OBJECTIVES: Develop a deep learning-based algorithm using the U-Net architecture to measure abdominal fat on computed tomography (CT) images.

Authors

  • Andrew T Grainger
    Departments of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.
  • Arun Krishnaraj
    Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia. Electronic address: [email protected].
  • Michael H Quinones
    Radiology & Medical Imaging, School of Medicine, Virginia.
  • Nicholas J Tustison
    a Department of Radiology and Medical Imaging.
  • Samantha Epstein
    Radiology & Medical Imaging, School of Medicine, Virginia.
  • Daniela Fuller
    School of Engineering and Applied Science, University of Virginia, 480 Ray C. Hunt Drive, Charlottesville, VA 22908.
  • Aakash Jha
    School of Arts & Sciences, University of Pennsylvania, Philadelphia PA, United States.
  • Kevin L Allman
    School of Engineering and Applied Science, University of Virginia, 480 Ray C. Hunt Drive, Charlottesville, VA 22908.
  • Weibin Shi
    Departments of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.

Keywords

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