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PyHIST: A Histological Image Segmentation Tool

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 histop...

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Detalles Bibliográficos
Autores principales: Muñoz-Aguirre, Manuel, Ntasis, Vasilis F., Rojas, Santiago, Guigó, Roderic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647117/
https://www.ncbi.nlm.nih.gov/pubmed/33075075
http://dx.doi.org/10.1371/journal.pcbi.1008349
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author Muñoz-Aguirre, Manuel
Ntasis, Vasilis F.
Rojas, Santiago
Guigó, Roderic
author_facet Muñoz-Aguirre, Manuel
Ntasis, Vasilis F.
Rojas, Santiago
Guigó, Roderic
author_sort Muñoz-Aguirre, Manuel
collection PubMed
description 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.
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spelling pubmed-76471172020-11-16 PyHIST: A Histological Image Segmentation Tool Muñoz-Aguirre, Manuel Ntasis, Vasilis F. Rojas, Santiago Guigó, Roderic PLoS Comput Biol Research Article 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. Public Library of Science 2020-10-19 /pmc/articles/PMC7647117/ /pubmed/33075075 http://dx.doi.org/10.1371/journal.pcbi.1008349 Text en © 2020 Muñoz-Aguirre et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Muñoz-Aguirre, Manuel
Ntasis, Vasilis F.
Rojas, Santiago
Guigó, Roderic
PyHIST: A Histological Image Segmentation Tool
title PyHIST: A Histological Image Segmentation Tool
title_full PyHIST: A Histological Image Segmentation Tool
title_fullStr PyHIST: A Histological Image Segmentation Tool
title_full_unstemmed PyHIST: A Histological Image Segmentation Tool
title_short PyHIST: A Histological Image Segmentation Tool
title_sort pyhist: a histological image segmentation tool
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647117/
https://www.ncbi.nlm.nih.gov/pubmed/33075075
http://dx.doi.org/10.1371/journal.pcbi.1008349
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