2026-04-13T04:17:13Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/457822025-12-12T03:06:15Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Muñoz-Aguirre, Manuel author Ntasis, Vasilis F. author Rojas, Santiago author Guigó Serra, Roderic author 2020-11-17T06:54:43Z 2020-11-17T06:54:43Z 2020 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. The authors received no specific funding for this work. M.M.-A. performs his research with support of pre-doctoral fellowship FPU15/03635 from Ministerio de Educación, Cultura y Deporte. (URL: http://www.mecd.gob.es/) Agencia Estatal de Investigación (AEI) and FEDER under project PGC2018-094017-B-I00 is also acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Hepatocellular carcinoma Preprocessing Histology Breast cancer Imaging techniques Machine learning Cancer detection and diagnosis Deep learning PyHIST: A Histological Image Segmentation Tool