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Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology
CADESS Med AB, Uppsala, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.ORCID iD: 0000-0003-2777-8114
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. CADESS Med AB, Uppsala, Sweden.
2019 (English)In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 7, article id 125Article in journal (Refereed) Published
Abstract [en]

Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists' workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Urological Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differentiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accurate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.

Place, publisher, year, edition, pages
2019. Vol. 7, article id 125
Keywords [en]
digital pathology, computational pathology, prostate cancer, prostate gland segmentation, histopathological stain, Picrosirius red, hematoxylin
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-391013DOI: 10.3389/fbioe.2019.00125ISI: 000475372000001PubMedID: 31334225OAI: oai:DiVA.org:uu-391013DiVA, id: diva2:1344500
Funder
Swedish Research Council, 2009-5418Swedish Research Council, 2012-3667Vinnova, 2017-00444Vinnova, 2018-02137Available from: 2019-07-05 Created: 2019-08-21 Last updated: 2019-08-30Bibliographically approved

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Tolf, AnnaDragomir, AncaCarlbom, Ingrid

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