Cargando…

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 This data is also related to the research paper entitle “Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation”, published in Computer Methods and...

Descripción completa

Detalles Bibliográficos
Autores principales: Bueno, Gloria, Gonzalez-Lopez, Lucia, Garcia-Rojo, Marcial, Laurinavicius, Arvydas, Deniz, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058889/
https://www.ncbi.nlm.nih.gov/pubmed/32154349
http://dx.doi.org/10.1016/j.dib.2020.105314
_version_ 1783503939468001280
author Bueno, Gloria
Gonzalez-Lopez, Lucia
Garcia-Rojo, Marcial
Laurinavicius, Arvydas
Deniz, Oscar
author_facet Bueno, Gloria
Gonzalez-Lopez, Lucia
Garcia-Rojo, Marcial
Laurinavicius, Arvydas
Deniz, Oscar
author_sort Bueno, Gloria
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7058889
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-70588892020-03-09 Data for glomeruli characterization in histopathological images Bueno, Gloria Gonzalez-Lopez, Lucia Garcia-Rojo, Marcial Laurinavicius, Arvydas Deniz, Oscar Data Brief Medicine and Dentistry 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. Elsevier 2020-02-24 /pmc/articles/PMC7058889/ /pubmed/32154349 http://dx.doi.org/10.1016/j.dib.2020.105314 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Medicine and Dentistry
Bueno, Gloria
Gonzalez-Lopez, Lucia
Garcia-Rojo, Marcial
Laurinavicius, Arvydas
Deniz, Oscar
Data for glomeruli characterization in histopathological images
title Data for glomeruli characterization in histopathological images
title_full Data for glomeruli characterization in histopathological images
title_fullStr Data for glomeruli characterization in histopathological images
title_full_unstemmed Data for glomeruli characterization in histopathological images
title_short Data for glomeruli characterization in histopathological images
title_sort data for glomeruli characterization in histopathological images
topic Medicine and Dentistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058889/
https://www.ncbi.nlm.nih.gov/pubmed/32154349
http://dx.doi.org/10.1016/j.dib.2020.105314
work_keys_str_mv AT buenogloria dataforglomerulicharacterizationinhistopathologicalimages
AT gonzalezlopezlucia dataforglomerulicharacterizationinhistopathologicalimages
AT garciarojomarcial dataforglomerulicharacterizationinhistopathologicalimages
AT laurinaviciusarvydas dataforglomerulicharacterizationinhistopathologicalimages
AT denizoscar dataforglomerulicharacterizationinhistopathologicalimages