Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

Journal: Journal of the American Society of Nephrology : JASN
Published Date: Sep 5, 2019

Abstract

BACKGROUND: The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS).

Authors

  • Meyke Hermsen
    Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Thomas de Bel
    Departments of Pathology and.
  • Marjolijn den Boer
    Departments of Pathology and.
  • Eric J Steenbergen
    Departments of Pathology and.
  • Jesper Kers
    Department of Pathology, Amsterdam Infection & Immunity, Amsterdam Cardiovascular Sciences, Amsterdam UMC, and.
  • Sandrine Florquin
    Department of Pathology, Amsterdam Infection & Immunity, Amsterdam Cardiovascular Sciences, Amsterdam UMC, and.
  • Joris J T H Roelofs
    Department of Pathology, Amsterdam Infection & Immunity, Amsterdam Cardiovascular Sciences, Amsterdam UMC, and.
  • Mark D Stegall
    Divisions of Transplantation surgery.
  • Mariam P Alexander
    William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; and.
  • Byron H Smith
    William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; and.
  • Bart Smeets
    Departments of Pathology and.
  • Luuk B Hilbrands
    Nephrology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Jeroen A W M van der Laak
    Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.

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