Artificial intelligence-based tissue segmentation and cell identification in multiplex-stained histological endometriosis sections.

Journal: Human reproduction (Oxford, England)
PMID: 39724530

Abstract

STUDY QUESTION: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?

Authors

  • Scott E Korman
    Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
  • Guus Vissers
    Department of Obstetrics and Gynaecology, Radboudumc, Nijmegen, The Netherlands.
  • Mark A J Gorris
    Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
  • Kiek Verrijp
    Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
  • Wouter P R Verdurmen
    Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
  • Michiel Simons
    Department of Pathology, Radboudumc, Nijmegen, the Netherlands. Electronic address: Michiel.Simons@radboudumc.nl.
  • Sebastien Taurin
    Department of Molecular Medicine, Arabian Gulf University, Manama, Kingdom of Bahrain.
  • Mai Sater
    Department of Medical Biochemistry, Arabian Gulf University, Manama, Kingdom of Bahrain.
  • Annemiek W Nap
    Department of Obstetrics and Gynaecology, Radboudumc, Nijmegen, The Netherlands.
  • Roland Brock
    Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.

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