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An image dataset related to automated macrophage detection in immunostained lymphoma tissue samples
Marcus Wagner1, Sarah Reinke2, René Hänsel1
1Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Härtelstr. 16-18, D-04107 Leipzig, Germany.
Insights
This study introduces a new image dataset for automated macrophage segmentation and counting in diffuse large B-cell lymphoma (DLBCL) tissue. This resource aids in analyzing the tumor microenvironment for improved DLBCL classification and prognosis.
Area of Science:
- Biomedical Imaging
- Computational Pathology
- Cancer Research
Background:
- Accurate analysis of tumor microenvironment, especially macrophages, is crucial for Diffuse Large B-cell Lymphoma (DLBCL) classification and prognosis.
- Current methods for macrophage analysis in DLBCL tissues rely on indirect gene expression profiling or laborious manual counting of immunohistochemically stained samples.
- Automated recognition of individual macrophages in immunohistochemistry (IHC)-stained tissue sections presents a significant technical challenge.
Purpose of the Study:
- To present a novel image dataset for the automated segmentation and counting of macrophages within DLBCL tissue sections.
- To facilitate research into the tumor microenvironment's role in DLBCL subtypes and clinical outcome prediction.
- To provide a valuable resource for developing and validating automated image analysis techniques for cancer pathology.
Main Methods:
- Generation of a dataset comprising fluorescence microscopy images from 44 DLBCL tumor subregions.
- Acquisition of multi-channel images (CD14, CD163, Pax5, DAPI) for specific cell marker identification.
- Application of Rudin-Osher-Fatemi denoising for image enhancement and automated generation of segmentation masks for macrophages, B-cells, and cell nuclei.
Main Results:
- The dataset includes multi-channel fluorescence microscopy images of DLBCL tissue sections.
- Generated images are processed with denoising techniques and include automatically created segmentation masks.
- Segmentation masks cover macrophages (CD14, CD163), B-cells (Pax5), and all cell nuclei (DAPI).
Conclusions:
- A comprehensive dataset of IHC-stained DLBCL specimens is provided.
- Automated segmentation masks for various cell populations (macrophages, B-cells, nuclei) are included.
- This dataset offers significant potential for reuse in automated image analysis research for DLBCL.
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.
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