| Title |
Data for glomeruli characterization in histopathological images / |
| Authors |
Bueno, Gloria ; Gonzalez-Lopez, Lucia ; Garcia-Rojo, Marcial ; Laurinavičius, Arvydas ; Deniz, Oscar |
| DOI |
10.1016/j.dib.2020.105314 |
| Full Text |
|
| Is Part of |
Data in brief.. Amsterdam : Elsevier. 2020, vol. 29, art. no. 105314, p. [1-5].. ISSN 2352-3409 |
| Keywords [eng] |
digital pathology ; global sclerotic glomerulus ; glomeruli identification ; normal glomerulus ; whole slide image |
| Abstract [eng] |
The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH 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. |
| Published |
Amsterdam : Elsevier |
| Type |
Journal article |
| Language |
English |
| Publication date |
2020 |
| CC license |
|
