Article open access publication

Quantitative analysis of myocardial tissue with digital autofluorescence microscopy

Journal of Pathology Informatics, Wolters Kluwer, ISSN 2153-3539

Volume 7, 1, 2016

DOI:10.4103/2153-3539.179908, Dimensions: pub.1047802426, PMC: PMC4837794, PMID: 27141321,



  1. (1) Department of Pathology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
  2. (2) University of Copenhagen, grid.5254.6, KU






BACKGROUND: The opportunity offered by whole slide scanners of automated histological analysis implies an ever increasing importance of digital pathology. To go beyond the importance of conventional pathology, however, digital pathology may need a basic histological starting point similar to that of hematoxylin and eosin staining in conventional pathology. This study presents an automated fluorescence-based microscopy approach providing highly detailed morphological data from unstained microsections. This data may provide a basic histological starting point from which further digital analysis including staining may benefit. METHODS: This study explores the inherent tissue fluorescence, also known as autofluorescence, as a mean to quantitate cardiac tissue components in histological microsections. Data acquisition using a commercially available whole slide scanner and an image-based quantitation algorithm are presented. RESULTS: It is shown that the autofluorescence intensity of unstained microsections at two different wavelengths is a suitable starting point for automated digital analysis of myocytes, fibrous tissue, lipofuscin, and the extracellular compartment. The output of the method is absolute quantitation along with accurate outlines of above-mentioned components. The digital quantitations are verified by comparison to point grid quantitations performed on the microsections after Van Gieson staining. CONCLUSION: The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue. The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

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