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Automated macrophage counting in DLBCL tissue samples

A ROF filter based approach
: Wagner, Markus; Hänsel, René; Reinke, Sarah; Richter, Julia; Altenbuchinger, Michael; Braumann, Ulf-Dietrich; Spang, Rainer; Loeffler, Markus; Klapper, Wolfram

Fulltext ()

Biological procedures online : BPO. Online journal 21 (2019), Article 13, 18 pp.
ISSN: 1480-9222
Journal Article, Electronic Publication
Fraunhofer IZI ()
Macrophage; CD14; Immunohistochemical staining; CD163; Automated cell counting; ROF filtering; Floating threshold; Rule-based detection

Background: For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples,it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now,macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing onlyindirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confrontedwith several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneousintensity of staining. Consequently, application of commercial software is largely restricted to very rough analysismodes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thusfailing to represent the heterogeneity of tumor microenvironment adequately.Methods: We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples,combining floating intensity thresholding and rule-based feature detection. Method is validated against manualcounts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and astraightforward machine-learning approach in a set of 50 test images. Further, the novel method and bothcommercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expressiondata available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumorsubregions for testing selection and subsampling strategies.Results: Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automateddetection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for thesame tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possiblewith results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5 ± 111.3μm2.Conclusions: ROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissuesamples. The method competes well with existing commercial software kits. In difference to them, it is fullyautomated, externally repeatable, independent on training data and completely documented. Comparison with geneexpression data indicates that image morphometry constitutes an independent source of information aboutantibody-polarized macrophage occurence and distribution.