Robust hierarchical density estimation and regression for re-stained histological whole slide image co-registration

For many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be pro...

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Bibliographic Details
Published in:PloS one 2019-07, Vol.14 (7), p.e0220074-e0220074
Main Authors: Jiang, Jun, Larson, Nicholas B, Prodduturi, Naresh, Flotte, Thomas J, Hart, Steven N
Format: Article
Language:English
Subjects:
Analysis
Benchmarking
Biology and Life Sciences
Cell density
Computer applications
Density
Digital imaging
EDTA
Eosin
Fluoresceins
Fluorescent Antibody Technique - methods
Hematoxylin
Histology
Identification methods
Image Processing, Computer-Assisted - methods
Image registration
Immunohistochemistry
Medical imaging
Medicine and Health Sciences
Methods
Pathology
Physical Sciences
Proteins
Registration
Regression analysis
Research and Analysis Methods
Robustness (mathematics)
Staining and Labeling - methods
Stains & staining
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Summary:For many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs-particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0220074