{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:12:14Z","timestamp":1772172734613,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008349","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000}}],"reference-count":23,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    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 (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/manuel-munoz-aguirre\/PyHIST\" xlink:type=\"simple\">https:\/\/github.com\/manuel-munoz-aguirre\/PyHIST<\/jats:ext-link>\n                    ), 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.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008349","type":"journal-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T13:40:09Z","timestamp":1603114809000},"page":"e1008349","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":42,"title":["PyHIST: A Histological Image Segmentation Tool"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6106-7351","authenticated-orcid":true,"given":"Manuel","family":"Mu\u00f1oz-Aguirre","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9951-6896","authenticated-orcid":true,"given":"Vasilis F.","family":"Ntasis","sequence":"additional","affiliation":[]},{"given":"Santiago","family":"Rojas","sequence":"additional","affiliation":[]},{"given":"Roderic","family":"Guig\u00f3","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"pcbi.1008349.ref001","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1002\/path.5331","article-title":"Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association","volume":"249","author":"E Abels","year":"2019","journal-title":"J Pathol"},{"key":"pcbi.1008349.ref002","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s13000-019-0921-2","article-title":"Next generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis","volume":"14","author":"AV Parwani","year":"2019","journal-title":"Diagn 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Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"pcbi.1008349.ref006","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-019-0112-2","article-title":"Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer","volume":"2","author":"K Nagpal","year":"2019","journal-title":"npj Digital Med"},{"key":"pcbi.1008349.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2019.03.014","article-title":"Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features","volume":"55","author":"T Qaiser","year":"2019","journal-title":"Med Image Anal"},{"key":"pcbi.1008349.ref008","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.patcog.2018.09.007","article-title":"Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images","volume":"86","author":"L Hou","year":"2019","journal-title":"Pattern 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adenocarcinoma in digital slides","volume":"9","author":"A Gertych","year":"2019","journal-title":"Sci Rep"},{"key":"pcbi.1008349.ref016","doi-asserted-by":"crossref","first-page":"e1007313","DOI":"10.1371\/journal.pcbi.1007313","article-title":"Orbit Image Analysis: An open-source whole slide image analysis tool","volume":"16","author":"M Stritt","year":"2020","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1008349.ref017","doi-asserted-by":"crossref","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: Open source software for digital pathology image analysis","volume":"7","author":"P Bankhead","year":"2017","journal-title":"Sci Rep"},{"key":"pcbi.1008349.ref018","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1093\/bioinformatics\/btw013","article-title":"Collaborative analysis of multi-gigapixel imaging data using Cytomine","volume":"32","author":"R 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