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Here we show how Res-CR-Net, a new type of fully convolutional neural network that does not adopt a U-Net architecture, excels at segmentation tasks traditionally considered very hard, like recognizing the contours of nuclei, cytoplasm and mitochondria in densely packed cells in either EM or LM\/FM images.<\/jats:p>","DOI":"10.1088\/2632-2153\/aba8e8","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T22:31:32Z","timestamp":1595543492000},"page":"045004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images"],"prefix":"10.1088","volume":"1","author":[{"given":"Hassan","family":"Abdallah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brent","family":"Formosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asiri","family":"Liyanaarachchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maranda","family":"Saigh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samantha","family":"Silvers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suzan","family":"Arslanturk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Douglas J","family":"Taatjes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Larsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhanu P","family":"Jena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6357-3530","authenticated-orcid":false,"given":"Domenico L","family":"Gatti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2020,9,17]]},"reference":[{"key":"mlstaba8e8bib1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","article-title":"A review on deep learning techniques applied to semantic segmentation","volume":"70","author":"Garcia-Garcia","year":"2018"},{"key":"mlstaba8e8bib2","doi-asserted-by":"publisher","first-page":"3431","DOI":"10.1109\/CVPR.2015.7298965","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"article-title":"DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","year":"2016","author":"Chen","key":"mlstaba8e8bib3"},{"article-title":"Rethinking atrous convolution for semantic image segmentation","year":"2017","author":"Chen","key":"mlstaba8e8bib4"},{"key":"mlstaba8e8bib5","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"MICCAI (Lecture Notes in Computer Science)"},{"key":"mlstaba8e8bib6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: deep learning for cell counting, detection, and morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2020-04-25","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-07-23","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-09-17","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}