Data for glomeruli characterization in histopathological images

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 an...

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Bibliographic Details
Published in:Data in brief 2020-04, Vol.29, p.105314-105314, Article 105314
Main Authors: Bueno, Gloria, Gonzalez-Lopez, Lucia, Garcia-Rojo, Marcial, Laurinavicius, Arvydas, Deniz, Oscar
Format: Article
Language:English
Subjects:
Digital pathology
Global sclerotic glomerulus
Glomeruli identification
Medicine and Dentistry
Normal glomerulus
Whole slide image
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Summary: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.
ISSN:2352-3409
2352-3409
DOI:10.1016/j.dib.2020.105314