Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
Department of Statistics and Operations Research, Universitat Politècnica de Catalunya (UPC), Barcelona, Catalonia, Spain.
PLoS Comput Biol. 2020 Oct 19;16(10):e1008349. doi: 10.1371/journal.pcbi.1008349. eCollection 2020 Oct.
The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods that aim to extract biological knowledge from histopathological images require several data preprocessing stages, creating an overhead before the proper analysis. Here we present PyHIST (https://github.com/manuel-munoz-aguirre/PyHIST), an easy-to-use, open source whole slide histological image tissue segmentation and preprocessing command-line tool aimed at tile generation for machine learning applications. From a given input image, the PyHIST pipeline i) optionally rescales the image to a different resolution, ii) produces a mask for the input image which separates the background from the tissue, and iii) generates individual image tiles with tissue content.
日益复杂的高分辨率图像获取方法的发展,导致了大量生物医学成像数据的产生,包括组织和器官的图像。目前许多旨在从组织病理学图像中提取生物学知识的机器学习方法都需要几个数据预处理阶段,在进行适当的分析之前会产生开销。在这里,我们介绍 PyHIST(https://github.com/manuel-munoz-aguirre/PyHIST),这是一个易于使用的、开源的全幻灯片组织学图像组织分割和预处理命令行工具,旨在为机器学习应用程序生成平铺图像。从给定的输入图像开始,PyHIST 管道 i)可选择将图像缩放到不同的分辨率,ii)为输入图像生成一个掩模,将背景与组织分开,iii)生成具有组织内容的单个图像平铺。