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An automatic entropy method to efficiently mask histology whole-slide images

Scientific Reports, ISSN: 2045-2322, Vol: 13, Issue: 1, Page: 4321
2023
  • 6
    Citations
  • 0
    Usage
  • 13
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    6
  • Captures
    13
  • Mentions
    1
    • News Mentions
      1
      • News
        1

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Data from University of Virginia Provide New Insights into Health and Medicine (An automatic entropy method to efficiently mask histology whole-slide images)

2023 APR 03 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Cardiovascular Daily -- Data detailed on agriculture have been presented. According to

Article Description

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.

Bibliographic Details

Song, Yipei; Cisternino, Francesco; Mekke, Joost M; de Borst, Gert J; de Kleijn, Dominique P V; Pasterkamp, Gerard; Vink, Aryan; Glastonbury, Craig A; van der Laan, Sander W; Miller, Clint L

Springer Science and Business Media LLC

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