ArticlePloS one2019
Robust hierarchical density estimation and regression for re-stained histological whole slide image co-registration.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- H&E-Referenced Multiplex Immunofluorescence Interpretation in TMA Cores: Spatial Co-localization, Cell Feature Validation, and Virtual H&E Generation.Research square · 2026Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- IntegrAlign: a comprehensive tool for multi-immunofluorescence panel integration through image alignment.Bioinformatics (Oxford, England) · 2025Article
- Towards H&E Referenced Multiplex Immunofluorescence Interpretation: Spatial Co-localization, Cell Feature Validation, and Virtual H&E Generation.Research square · 2025Article
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- Towards Realistic 3D Models of Tumor Vascular Networks.Cancers · 2023Article
- Whole-Slide Imaging, Mutual Information Registration for Multiplex Immunohistochemistry and Immunofluorescence.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- Virtual alignment of pathology image series for multi-gigapixel whole slide images.Nature communications · 2023Article
- Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel-referenced Evaluation.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2023Article
- Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides.Journal of neuropathology and experimental neurology · 2023Article
- Fast cross-staining alignment of gigapixel whole slide images with application to prostate cancer and breast cancer analysis.Scientific reports · 2022Article
- Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.Veterinary pathology · 2022Article
- Regional registration of whole slide image stacks containing major histological artifacts.BMC bioinformatics · 2020Article
- Image-to-image translation for automatic ink removal in whole slide images.Journal of medical imaging (Bellingham, Wash.) · 2020Article
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Authors and funding
5 authors.
Funding
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Abstract
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.
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