Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

Journal: Journal of the American Society of Nephrology : JASN
PMID: 33154175

Abstract

BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation.

Authors

  • Nassim Bouteldja
    Institute of Medical Informatics, University of Lübeck, Germany.
  • Barbara M Klinkhammer
  • Roman D Bülow
    Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.
  • Patrick Droste
    Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.
  • Simon W Otten
    Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.
  • Saskia Freifrau von Stillfried
    Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.
  • Julia Moellmann
    Department of Cardiology and Vascular Medicine, RWTH Aachen University Hospital, Aachen, Germany.
  • Susan M Sheehan
    The Jackson Laboratory , Bar Harbor, Maine.
  • Ron Korstanje
    The Jackson Laboratory, Bar Harbor, ME, United States of America.
  • Sylvia Menzel
    Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany.
  • Peter Bankhead
    MRC Institute of Genetics and Molecular Medicine, University of Ed-inburgh, Edinburgh, UK.
  • Matthias Mietsch
    Laboratory Animal Science Unit, German Primate Center, Goettingen, Germany.
  • Charis Drummer
    Platform Degenerative Diseases, German Primate Center, Goettingen, Germany.
  • Michael Lehrke
    Department of Cardiology and Vascular Medicine, RWTH Aachen University Hospital, Aachen, Germany.
  • Rafael Kramann
    Department of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.
  • Jürgen Floege
    Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany.
  • Peter Boor
    Institute of Pathology, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.
  • Dorit Merhof
    Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).

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