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Related Experiment Video

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Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough?

Jose L Agraz1,2,3, Caleb M Grenko4, Andrew A Chen5

  • 1Center for Biomedical Image Computing and Analytics (CBICA) Philaldelphia PA 19139 USA.

IEEE Open Journal of Engineering in Medicine and Biology
|March 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for normalizing Hematoxylin & Eosin (H&E) stained whole slide images (WSIs) using an optimal subset of slides. This approach enhances the accuracy and reliability of computational pathology analyses by reducing color variations.

Keywords:
Ivy GAPPareto principle rulePower lawStain normalization bias problemWhole slide image

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Histopathology image analysis

Background:

  • Hematoxylin & Eosin (H&E) staining is crucial for disease diagnosis via whole slide image (WSI) analysis.
  • Color variations in H&E slides due to staining and equipment introduce inaccuracies in computational analysis.
  • Current normalization methods using a single WSI reference are prone to bias.

Purpose of the Study:

  • To determine the optimal number of WSIs required to create a representative reference for normalization.
  • To develop an aggregate-based stain normalization method for improved computational pathology.
  • To enhance the robustness and reproducibility of WSI analysis.

Main Methods:

  • Utilized 1,864 IvyGAP WSIs as a cohort, creating 200 subsets of varying sizes.
  • Calculated Wasserstein Distances and standard deviations to find the optimal subset size via the Pareto Principle.
  • Applied structure-preserving color normalization using aggregate histograms and stain-vectors from the optimal subset.

Main Results:

  • The Pareto Principle identified an optimal WSI-Cohort-Subset size for representative normalization.
  • Aggregate-based normalization demonstrated swift CIELAB color space convergence across WSI-cohorts.
  • Quantitative and qualitative assessments confirmed the effectiveness of the proposed normalization method.

Conclusions:

  • Aggregate-based stain normalization using an optimal WSI subset improves computational pathology robustness.
  • This method mitigates data domain shift and enhances generalization in WSI analysis.
  • The findings support increased reproducibility and integrity in digital pathology.