Marcus Wagner1, Sarah Reinke2, René Hänsel1, Wolfram Klapper2, Ulf-Dietrich Braumann3,4. 1. Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Härtelstr. 16-18, D-04107 Leipzig, Germany. 2. Department of Pathology, Hematopathology Section and Lymph Node Registry, University of Kiel/University Hospital Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 14, D-24105 Kiel, Germany. 3. Faculty of Engineering, Leipzig University of Applied Sciences (HTWK), P.O.B. 30 11 66, D-04251 Leipzig, Germany. 4. Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstr. 1, D-04103 Leipzig, Germany.
Abstract
BACKGROUND: We present an image dataset related to automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections. For the classification of DLBCL subtypes, as well as for providing a prognosis of the clinical outcome, the analysis of the tumor microenvironment and, particularly, of the different types and functions of tumor-associated macrophages is indispensable. Until now, however, most information about macrophages has been obtained either in a completely indirect way by gene expression profiling or by manual counts in immunohistochemically (IHC) fluorescence-stained tissue samples while automated recognition of single IHC stained macrophages remains a difficult task. In an accompanying publication, a reliable approach to this problem has been established, and a large set of related images has been generated and analyzed. RESULTS: Provided image data comprise (i) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at 4 channels corresponding to CD14, CD163, Pax5, and DAPI; (ii) "cartoon-like" total variation-filtered versions of these images, generated by Rudin-Osher-Fatemi denoising; (iii) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel; and (iv) automatically generated segmentation masks for macrophages (using information from CD14 and CD163 channels), B-cells (using information from Pax5 channel), and all cell nuclei (using information from DAPI channel). CONCLUSIONS: 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.
BACKGROUND: We present an image dataset related to automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections. For the classification of DLBCL subtypes, as well as for providing a prognosis of the clinical outcome, the analysis of the tumor microenvironment and, particularly, of the different types and functions of tumor-associated macrophages is indispensable. Until now, however, most information about macrophages has been obtained either in a completely indirect way by gene expression profiling or by manual counts in immunohistochemically (IHC) fluorescence-stained tissue samples while automated recognition of single IHC stained macrophages remains a difficult task. In an accompanying publication, a reliable approach to this problem has been established, and a large set of related images has been generated and analyzed. RESULTS: Provided image data comprise (i) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at 4 channels corresponding to CD14, CD163, Pax5, and DAPI; (ii) "cartoon-like" total variation-filtered versions of these images, generated by Rudin-Osher-Fatemi denoising; (iii) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel; and (iv) automatically generated segmentation masks for macrophages (using information from CD14 and CD163 channels), B-cells (using information from Pax5 channel), and all cell nuclei (using information from DAPI channel). CONCLUSIONS: 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.
Authors: David W Scott; George W Wright; P Mickey Williams; Chih-Jian Lih; William Walsh; Elaine S Jaffe; Andreas Rosenwald; Elias Campo; Wing C Chan; Joseph M Connors; Erlend B Smeland; Anja Mottok; Rita M Braziel; German Ott; Jan Delabie; Raymond R Tubbs; James R Cook; Dennis D Weisenburger; Timothy C Greiner; Betty J Glinsmann-Gibson; Kai Fu; Louis M Staudt; Randy D Gascoyne; Lisa M Rimsza Journal: Blood Date: 2014-01-07 Impact factor: 22.113
Authors: Lee Ad Cooper; Elizabeth G Demicco; Joel H Saltz; Reid T Powell; Arvind Rao; Alexander J Lazar Journal: J Pathol Date: 2018-02-22 Impact factor: 7.996
Authors: Marcus Wagner; René Hänsel; Sarah Reinke; Julia Richter; Michael Altenbuchinger; Ulf-Dietrich Braumann; Rainer Spang; Markus Löffler; Wolfram Klapper Journal: Biol Proced Online Date: 2019-07-01 Impact factor: 3.244