An automatic entropy method to efficiently mask histology whole-slide images
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
(2023) Scientific Reports, volume 13, issue 1
(Article)
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,
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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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Keywords: Entropy, Histological Techniques, Humans, Image Processing, Computer-Assisted/methods, Machine Learning, Plaque, Atherosclerotic/diagnostic imaging, General, Journal Article
ISSN: 2045-2322
Publisher: Nature Publishing Group
Note: Funding Information: Funding for this research was provided by National Institutes of Health (NIH) grant nos. R00HL125912 and R01HL14823 (to CLM), a Leducq Foundation Transatlantic Network of Excellence (‘PlaqOmics’) grant no. 18CVD02 (to CLM and SWvdL), and the EU H2020 TO_AITION Grant no. 848146 (to SWvdL). Funding Information: We also acknowledge support from the Netherlands CardioVascular Research Initiative of the Netherlands Heart Foundation (CVON 2011/B019 and CVON 2017-20: Generating the best evidence-based pharmaceutical targets for atherosclerosis [GENIUS I&II]), the ERA-CVD program ‘druggable-MI-targets’ (Grant No: 01KL1802), and the Chan Zuckerberg Initiative Foundation Data Insights program (Grant No: DI-092).We would also like to thank all the (former) employees involved in the Athero-Express Biobank Study of the Departments of Surgery of the St. Antonius Hospital Nieuwegein and University Medical Center Utrecht for their continuing work. We would like to thank (in no particular order) Marijke Linschoten, Arjan Samani, Petra H. Homoed-van der Kraak, Tim Bezemer, Tim van de Kerkhof, Joyce Vrijenhoek, Evelyn Velema, Ben van Middelaar, Sander Reukema, Robin Reijers, Joëlle van Bennekom, and Bas Nelissen. Lastly, we would like to thank all participants of the Athero-Express Biobank Study; without you these studies would not be possible. Publisher Copyright: © 2023, The Author(s).
(Peer reviewed)