Data for glomeruli characterization in histopathological images

Published: 5 February 2020| Version 3 | DOI: 10.17632/k7nvtgn2x6.3
Gloria Bueno,


The data presented hereis 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 (DOI: 10.1016/j.cmpb.2019.105273) . In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology. Parameters for data collection: Tissue samples were collected with a biopsy needle having an outer diameter between 100μm and 300μm. Afterwards, paraffin blocks were prepared using tissue sections of 4μm and stained using Periodic acid–Schiff (PAS). Then, images at 20x magnification were selected. Description of data collection: The tissue samples were scanned at 20x with a Leica Aperio ScanScope CS scanner. Data format: DATASET_A_DIB: Raw data, original images in SVS format. DATASET_B_DIB: Classified: Detected glomeruli to be used for classification in PNG format. The data is composed of two datasets: 1.) DATASET_A: Raw data with 31 whole slide images (WSI) in SVS format. The size of the WSI range between 21651x10498 pixels and 49799 x 32359 pixles acquired at 20x. The images contain different types of glomeruli that were detected using the algorithms explained at the following article []. The detected glomeruli are provided in DATASET_B. 2.) DATASET_B: 2,340 images with a single glomerulous, 1,170 normal glomeruli and 1,170 sclerosed glomeruli. All of them are in PNG format. Value of the Data · These data can be used for benchmarking to encourage further research on AI methods applied to digital pathology in nephrology. · The additional value of this data is that it has been acquired and evaluated by expert pathologists from different European countries. · All researches in digital pathology can benefit from these data, to test classification algorithms. And particularly for glomeruli identification in nephrology studies. · This data can be used for further development and new experiments in glomeruli classification with more classes, like focal glomeruli besides normal and sclerotic glomeruli.



Universidad de Castilla-La Mancha


Renal Glomerulus, Digital Pathology, Glomerular Disorder