Bueno Gloria, Gonzalez-Lopez Lucia, Garcia-Rojo Marcial, Laurinavicius Arvydas, Deniz Oscar
VISILAB, Universidad de Castilla-La Mancha, Ciudad Real, Spain.
Pathology Department, Hospital Universitario de Ciudad Real, Ciudad Real, Spain.
Data Brief. 2020 Feb 24;29:105314. doi: 10.1016/j.dib.2020.105314. eCollection 2020 Apr.
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.
本文所呈现的数据是欧洲项目AIDPATH生成的全切片成像（WSI）数据集的一部分。该数据还与发表在《计算机方法与生物医学程序》杂志上的题为“使用语义分割在全切片图像中识别肾小球硬化”的研究论文相关[1]。在那篇文章中，介绍并讨论了基于深度学习的不同肾小球分割方法及其对正常和硬化性肾小球的分类。这里描述并提供了所使用的原始数据。此外，检测到的肾小球也作为单独的图像文件提供。这些数据将促进对人工智能（AI）方法的研究，创建并比较新的算法，并衡量它们在定量肾脏病理学中的可用性。
