ArticlePloS one2017
An alternative reference space for H&E color normalization.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.The Journal of pathology · 2019Guideline
- Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.Journal of advanced research · 2026Review
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Color restoration based on digital pathology image.PloS one · 2023Article
- Deep learning-based framework for slide-based histopathological image analysis.Scientific reports · 2022Article
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
- Classification of Mouse Lung Metastatic Tumor with Deep Learning.Biomolecules & therapeutics · 2022Article
- Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.Cancers · 2022Review
- Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.Journal of medical systems · 2021Review
- Introduction to digital pathology and computer-aided pathology.Journal of pathology and translational medicine · 2020Review
- Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.NPJ breast cancer · 2020Review
- Learning to Evaluate Color Similarity for Histopathology Images using Triplet Networks.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2019Article
- Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association.Journal of pathology informatics · 2019Review
- An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images.BMC biomedical engineering · 2019Article
- Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images.Frontiers in bioengineering and biotechnology · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital imaging of H&E stained slides has enabled the application of image processing to support pathology workflows. Potential applications include computer-aided diagnostics, advanced quantification tools, and innovative visualization platforms. However, the intrinsic variability of biological tissue and the vast differences in tissue preparation protocols often lead to significant image variability that can hamper the effectiveness of these computational tools. We developed an alternative representation for H&E images that operates within a space that is more amenable to many of these image processing tools. The algorithm to derive this representation operates by exploiting the correlation between color and the spatial properties of the biological structures present in most H&E images. In this way, images are transformed into a structure-centric space in which images are segregated into tissue structure channels. We demonstrate that this framework can be extended to achieve color normalization, effectively reducing inter-slide variability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.