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

Scott E Korman1, Guus Vissers2, Mark A J Gorris1,3

  • 1Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.

Human Reproduction (Oxford, England)
|December 26, 2024
PubMed
Summary

This study developed an AI-driven method for analyzing endometriosis tissue sections. Combining machine learning for segmentation and deep learning for cell identification improves histological analysis of endometriosis.

Keywords:
3,3′-diaminobenzidineartificial intelligencecell proliferationcomputer-assisted image analysisendometriosisfibrosisinflammationmultiplex immunofluorescencesupervised machine learning

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Area of Science:

  • Reproductive Medicine and Pathology
  • Computational Biology and Bioinformatics
  • Artificial Intelligence in Histopathology

Background:

  • Endometriosis is a complex gynecological condition with significant patient and subtype variability.
  • Understanding endometriosis tissue composition requires precise segmentation and cell counting.
  • Current histological analysis methods may not fully capture the complexity of endometriosis.

Purpose of the Study:

  • To establish an optimal method for tissue segmentation and cell counting in multichannel-stained endometriosis sections.
  • To leverage artificial intelligence for automated histological analysis of endometriosis.
  • To enhance the understanding of endometriosis tissue composition and cellular makeup.

Main Methods:

  • Utilized formalin-fixed, paraffin-embedded endometriosis tissue samples from eight patients.
  • Developed a 6-plex immunofluorescence panel with a nuclear stain for multiplex immunohistochemistry.
  • Employed AI-based tissue and cell phenotyping, including machine learning for segmentation and deep learning for cell identification.

Main Results:

  • An endometriosis-specific multiplex panel (PanCK, CD10, α-SMA, calretinin, CD45, Ki67, DAPI) successfully distinguished tissue structures.
  • Machine learning provided reliable tissue substructure segmentation.
  • A segmentation-free deep learning algorithm demonstrated superior performance for cell identification.

Conclusions:

  • A combined approach of machine learning for tissue segmentation and deep learning for cell identification offers high performance in automated endometriosis histology.
  • Multiplex staining combined with AI-based cell phenotyping shows significant potential for endometriosis research.
  • The deep learning method's ability to phenotype cells in untrained tissue types highlights its utility for heterogeneous endometriosis samples.