ArticleBMC bioinformatics2020
Regional registration of whole slide image stacks containing major histological artifacts.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- Longitudinal evaluation of tumor-infiltrating lymphocyte scoring using automated region of interest registration in breast cancer.Breast cancer research : BCR · 2026Article
- SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.Briefings in bioinformatics · 2026Article
- Quantitative Stain Mapping in X-Ray Virtual Histology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- Towards H&E Referenced Multiplex Immunofluorescence Interpretation: Spatial Co-localization, Cell Feature Validation, and Virtual H&E Generation.Research square · 2025Article
- 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
- Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images.Cancers · 2023Article
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14 authors.
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Abstract
backgroundHigh resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration.
resultsUsing mean similarity index as the metric, the proposed algorithm (mean ± SD [Formula: see text]) followed by a fine registration algorithm ([Formula: see text]) outperformed the state-of-the-art linear whole tissue registration algorithm ([Formula: see text]) and the regional version of this algorithm ([Formula: see text]). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: [Formula: see text], regional: [Formula: see text]) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: [Formula: see text] , patch size 512: [Formula: see text]) for medical images.
conclusionUsing multi-scale attention mechanism leads to a more robust and accurate solution to the problem of regional registration of whole slide images corrupted in some parts by major histological artifacts in the imaged tissue.
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