• Medientyp: E-Artikel
  • Titel: Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine
  • Beteiligte: Sziva, Réka Eszter; Ács, Júlia; Tőkés, Anna-Mária; Korsós-Novák, Ágnes; Nádasy, György L.; Ács, Nándor; Horváth, Péter Gábor; Szabó, Anett; Ke, Haoran; Horváth, Eszter Mária; Kopa, Zsolt; Várbíró, Szabolcs
  • Erschienen: MDPI AG, 2022
  • Erschienen in: Life, 12 (2022) 2, Seite 189
  • Sprache: Englisch
  • DOI: 10.3390/life12020189
  • ISSN: 2075-1729
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  • Beschreibung: Infertility is increasing worldwide; male factors can be identified in nearly half of all infertile couples. Histopathologic evaluation of testicular tissue can provide valuable information about infertility; however, several different evaluation methods and semi-quantitative score systems exist. Our goal was to describe a new, accurate and easy-to-use quantitative computer-based histomorphometric-mathematical image analysis methodology for the analysis of testicular tissue. On digitized, original hematoxylin-eosin (HE)-stained slides (scanned by slide-scanner), quantitatively describable characteristics such as area, perimeter and diameter of testis cross-sections and of individual tubules were measured with the help of continuous magnification. Immunohistochemically (IHC)-stained slides were digitized with a microscope-coupled camera, and IHC-staining intensity measurements on digitized images were also taken. Suggested methods are presented with mathematical equations, step-by-step detailed characterization and representative images are given. Our novel quantitative histomorphometric-mathematical image analysis method can improve the reproducibility, objectivity, quality and comparability of andrological-reproductive medicine research by recognizing even the mild impairments of the testicular structure expressed numerically, which might not be detected with the present semi-quantitative score systems. The technique is apt to be subjected to further automation with machine learning and artificial intelligence and can be named ‘Computer-Assisted or -Aided Testis Histology’ (CATHI).
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