ArticleScientific reports2023
An automatic entropy method to efficiently mask histology whole-slide images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep Learning-Based Multimodal Fusion of Whole-Slide Images and RNA Sequencing Identifies Survival-Relevant Glioblastoma Clusters.Cancer medicine · 2026Article
- HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics.bioRxiv : the preprint server for biology · 2026Article
- Efficient Tissue Detection in Whole-Slide Images Using Classical and Hybrid Methods: Benchmark on TCGA Cancer Cohorts.Cancers · 2025Article
- NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma.Journal of imaging informatics in medicine · 2025Article
- Review
- An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.Sensors (Basel, Switzerland) · 2024Article
- ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.Frontiers in bioengineering and biotechnology · 2024Article
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10 authors.
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Abstract
Tissue segmentation of histology whole-slide images (WSI) remains a critical task in automated digital pathology workflows for both accurate disease diagnosis and deep phenotyping for research purposes. This is especially challenging when the tissue structure of biospecimens is relatively porous and heterogeneous, such as for atherosclerotic plaques. In this study, we developed a unique approach called 'EntropyMasker' based on image entropy to tackle the fore- and background segmentation (masking) task in histology WSI. We evaluated our method on 97 high-resolution WSI of human carotid atherosclerotic plaques in the Athero-Express Biobank Study, constituting hematoxylin and eosin and 8 other staining types. Using multiple benchmarking metrics, we compared our method with four widely used segmentation methods: Otsu's method, Adaptive mean, Adaptive Gaussian and slideMask and observed that our method had the highest sensitivity and Jaccard similarity index. We envision EntropyMasker to fill an important gap in WSI preprocessing, machine learning image analysis pipelines, and enable disease phenotyping beyond the field of atherosclerosis.
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