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Author: Marcus Wagner2
Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach
MMML Demonstrators - Molecular Mechanisms in Malignant Lymphomas - Demonstrators of Personalized Medicine

Abstract (Expand)

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 … manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. Our method is deterministic, fully automated, externally repeatable, independent on training data and -- in difference to most commercial software kits -- completely documented. Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Subsampling within tumor subregions is possible with results almost identical to full sampling. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels, thus indicating that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.

Authors: Marcus Wagner, René Hänsel, Sarah Reinke, Julia Richter, Michael Altenbuchinger, Ulf-Dietrich Braumann, Rainer Spang, Markus Löffler, Wolfram Klapper

Date Published: No date defined

Publication Type: Not specified

Human Diseases: diffuse large B-cell lymphoma

An image dataset related to automated macrophage detection in immunostained lymphoma tissue samples
MMML Demonstrators - Molecular Mechanisms in Malignant Lymphomas - Demonstrators of Personalized Medicine

Abstract (Expand)

A large set of IHC stained DLBCL specimens is provided together with segmentation masks for different cell populations generated by a reference method for automated image analysis, thus featuring … considerable reuse potential. Provided image data comprise a) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at four channels corresponding to CD14, CD163, Pax5 and DAPI; b) cartoon-filtered versions of these images, generated by Rudin-Osher-Fatemi (ROF) denoising; c) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel, and d) automatically generated segmentation masks for macrophages, B-cells and the total of cell nuclei, using information from CD14, CD163, Pax5 and DAPI channels, respectively.

Authors: Marcus Wagner, Sarah Reinke, René Hänsel, Wolfram Klapper, Ulf-Dietrich Braumann

Date Published: 12th Mar 2020

Publication Type: Journal article

Human Diseases: diffuse large B-cell lymphoma

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