Muñoz-Aguirre, ManuelNtasis, Vasilis F.Rojas, SantiagoGuigó Serra, Roderic2020-11-172020-11-172020Muñoz-Aguirre M, Ntasis VF, Rojas S, Guigó R. PyHIST: A Histological Image Segmentation Tool. PLoS Comput Biol. 2020; 16(10):e1008349. DOI: 10.1371/journal.pcbi.10083491553-734Xhttp://hdl.handle.net/10230/45782The 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.application/pdfeng© 2020 Muñoz-Aguirre et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1371/journal.pcbi.1008349Hepatocellular carcinomaPreprocessingHistologyBreast cancerImaging techniquesMachine learningCancer detection and diagnosisDeep learninginfo:eu-repo/semantics/openAccess