Deep learning-based histopathologic segmentation of peritubular capillaries in kidney transplant biopsies.

Journal: Computers in biology and medicine
Published Date: May 24, 2025

Abstract

BACKGROUND: Assessing the extent of inflammation in peritubular capillaries (PTCs) is important for diagnosing antibody-mediated rejection in kidney transplant biopsies. However, this assessment is time-consuming and suffers from interobserver variability, making it a promising candidate for automation. This study introduces a deep learning-based approach for PTCs detection and segmentation in PAS-stained kidney transplant biopsies, providing a first step towards automated scoring of peritubular capillaritis (ptc).

Authors

  • Dominique van Midden
    Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address: Dominique.vanMidden@radboudumc.nl.
  • Linda Studer
    Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland.
  • Meyke Hermsen
    Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Eric J Steenbergen
    Departments of Pathology and.
  • Jesper Kers
    Department of Pathology, Amsterdam Infection & Immunity, Amsterdam Cardiovascular Sciences, Amsterdam UMC, and.
  • Nicolas Kozakowski
    Department of Pathology, Medical University of Vienna, Vienna, Austria.
  • Željko Kikić
    Department of Urology, Medical University of Vienna, Vienna, Austria.
  • 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.

Keywords

External Resources

Popular Topics
Recent Journals