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Adaptive color deconvolution for histological WSI normalization.

Yushan Zheng1, Zhiguo Jiang1, Haopeng Zhang1

  • 1Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China.

Computer Methods and Programs in Biomedicine
|February 5, 2019
PubMed
Summary
This summary is machine-generated.

Color normalization of whole slide images (WSIs) is crucial for accurate computer-aided diagnosis (CAD). This study introduces an adaptive color deconvolution (ACD) algorithm that effectively normalizes stain colors in WSIs, improving cancer detection accuracy.

Keywords:
CADColor normalizationDigital pathologyStain separationWSI

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Medical Image Analysis

Background:

  • Color variations in histological images hinder reliable computer-aided diagnosis (CAD) systems.
  • Inconsistent staining and scanning affect diagnostic accuracy and CAD performance.
  • Effective color normalization is essential for digital histology.

Purpose of the Study:

  • To develop a novel adaptive color deconvolution (ACD) algorithm for stain separation and color normalization of hematoxylin-eosin-stained whole slide images (WSIs).
  • To improve the accuracy and robustness of CAD systems by addressing color variability in WSIs.

Main Methods:

  • Proposed an adaptive color deconvolution (ACD) algorithm integrating multiple staining prior knowledges.
  • Employed an integrated optimization to simultaneously estimate stain separation and color normalization parameters.
  • Utilized pixel-wise operations for efficient computation and applicability to WSIs.

Main Results:

  • The ACD method achieved consistent color normalization across diverse WSI datasets (breast, lung, cervix cancers).
  • Demonstrated a low normalization failure rate (0.4%) with effectively avoided artifacts.
  • Significantly improved cancer image classification AUC from 0.842 to 0.914 when applied to CAD methods.
  • Efficiently solved the ACD model in an average of 2.97 seconds.

Conclusions:

  • The proposed ACD model is effective for color normalization of hematoxylin-eosin-stained WSIs with varied appearances.
  • The model is robust for WSIs containing different lesions and suitable for automatic CAD systems.
  • Efficient computation and improved cancer recognition performance make it adequate for developing CAD programs.