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Data for glomeruli characterization in histopathological images

Authors :
Oscar Deniz
Marcial García-Rojo
Gloria Bueno
Lucia Gonzalez-Lopez
Arvydas Laurinavicius
Source :
Data in Brief, Vol 29, Iss, Pp-(2020), Data in brief, Amsterdam : Elsevier, 2020, vol. 29, art. no. 105314, p. [1-5], Data in Brief
Publication Year :
2020
Publisher :
Elsevier, 2020.

Abstract

The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH 2 This data is also related to the research paper entitle “Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation”, published in Computer Methods and Programs in Biomedicine Journal [1]. In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. The raw data used is described and provided here. In addition, the detected glomeruli are also provided as individual image files. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology. Keywords: Glomeruli identification, Normal glomerulus, Global sclerotic glomerulus, Whole slide image, Digital pathology

Details

Language :
English
ISSN :
23523409
Volume :
29
Database :
OpenAIRE
Journal :
Data in Brief
Accession number :
edsair.doi.dedup.....b7e2b1e022806adddfe226e9a2e471ae