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Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues
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Mapping stain distribution in pathology slides using whole slide imaging.

Fang-Cheng Yeh1, Qing Ye2, T Kevin Hitchens2

  • 1Department of Biomedical Engineering, Pittsburgh, Pennsylvania, USA ; Department of Biological Science, Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Journal of Pathology Informatics
|March 28, 2014
PubMed
Summary
This summary is machine-generated.

A new computational method analyzes whole slide imaging (WSI) data to quantify stain density, correlating well with manual counts. This aids in diagnosing cardiac transplant rejection and ischemic injury.

Keywords:
Stain distribution imagestain recognitionwhole slide imaging

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Area of Science:

  • Computational pathology
  • Digital imaging analysis
  • Biomedical image processing

Background:

  • Whole slide imaging (WSI) generates large datasets requiring advanced computational analysis.
  • Current methods for analyzing digitized pathology slides are limited.

Purpose of the Study:

  • To develop and validate a computational method for recognizing stains and calculating stain density in WSI data.
  • To correlate WSI stain density with cellular Magnetic Resonance (MR) imaging data.

Main Methods:

  • A novel algorithm recognizes stains in WSI data and uses kernel density estimation to compute stain density.
  • Validation involved rat models of cardiac allograft rejection and ischemia/reperfusion injury.
  • Immunohistochemistry (IHC) was used to label macrophages, followed by WSI digitization and pixel-wise stain classification.

Main Results:

  • The stain recognition algorithm demonstrated strong agreement with manual counting (correlation coefficient of 0.8961).
  • Calculated stain density patterns correlated with cellular MR imaging data for macrophage detection.

Conclusions:

  • The developed method offers a new imaging modality for clinical diagnosis.
  • This approach validates and correlates cellular MR imaging data for tracking immune-cell infiltration in cardiac conditions.