dc.publisher: Nature Publishing Group
access_endpoint: https://www.nature.com/platform/readcube-access
og:image: https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41592-018-0216-7/MediaObjects/41592_2018_216_Fig1_HTML.png
WT.cg_s: Article
twitter:card: summary
og:site_name: Nature Methods
citation_reference: citation_journal_title=Science; citation_title=Optical sectioning deep inside live embryos by selective plane illumination microscopy; citation_author=J Huisken; citation_volume=305; citation_publication_date=2004; citation_pages=1007-1009; citation_doi=10.1126/science.1100035; citation_id=CR1
citation_journal_title: Nature Methods
dc.rights: ©2020 Macmillan Publishers Limited. All Rights Reserved.
og:description: Content-aware image restoration (CARE) uses deep learning to improve microscopy images. CARE bypasses the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.
prism.issn: 1548-7105
WT.cg_n: Nature Methods
prism.number: 12
citation_issn: 1548-7105
twitter:image:alt: Content cover image
WT.z_subject_term_id: image-processing;machine-learning;microscopy;software
dc:title: Content-aware image restoration: pushing the limits of fluorescence microscopy | Nature Methods
citation_language: en
Content-Encoding: UTF-8
WT.z_cg_type: Nature Research Journals
citation_pdf_url: https://www.nature.com/articles/s41592-018-0216-7.pdf
robots: noarchive
WT.z_primary_atype: Research
citation_lastpage: 1097
DOI: 10.1038/s41592-018-0216-7
application-name: Nature
citation_journal_abbrev: Nat Methods
prism.rightsAgent: journalpermissions@springernature.com
citation_author: Martin Weigert
dc.date: 2018-11-26
WT.z_subject_term: Image processing;Machine learning;Microscopy;Software
WT.z_bandiera_abtest: a
citation_issue: 12
prism.volume: 15
WT.template: oscar
prism.publicationName: Nature Methods
citation_doi: 10.1038/s41592-018-0216-7
dc.title: Content-aware image restoration: pushing the limits of fluorescence microscopy
prism.url: https://www.nature.com/articles/s41592-018-0216-7
citation_volume: 15
dc.language: En
Content-Language: en
msapplication-config: /static/browserconfig.e35b3b052c.xml
citation_publication_date: 2018/12
theme-color: #000000
prism.endingPage: 1097
citation_title: Content-aware image restoration: pushing the limits of fluorescence microscopy
citation_author_institution: Center for Systems Biology Dresden, Dresden, Germany
access: Yes
citation_publisher: Nature Publishing Group
dc.format: text/html
description: Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to tenfold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times-higher frame rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME. Content-aware image restoration (CARE) uses deep learning to improve microscopy images. CARE bypasses the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.
title: Content-aware image restoration: pushing the limits of fluorescence microscopy | Nature Methods
twitter:image: https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41592-018-0216-7/MediaObjects/41592_2018_216_Fig1_HTML.png
citation_online_date: 2018/11/26
twitter:site: @naturemethods
dc.source: Nature Methods 2018 15:12
dc.type: OriginalPaper
dc.copyright: 2018 The Author(s), under exclusive licence to Springer Nature America, Inc.
dc.creator: Martin Weigert
citation_fulltext_html_url: https://www.nature.com/articles/s41592-018-0216-7
WT.page_categorisation: Article_HTML
prism.publicationDate: 2018-11-26
Content-Type: text/html; charset=UTF-8
journal_id: 41592
X-Parsed-By: org.apache.tika.parser.DefaultParser
dc.description: Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to tenfold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times-higher frame rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME. Content-aware image restoration (CARE) uses deep learning to improve microscopy images. CARE bypasses the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.
twitter:title: Content-aware image restoration: pushing the limits of fluorescence mi
og:type: article
citation_article_type: Article
og:title: Content-aware image restoration: pushing the limits of fluorescence microscopy
prism.doi: doi:10.1038/s41592-018-0216-7
msapplication-TileColor: #000000
X-UA-Compatible: IE=edge
citation_firstpage: 1090
WT.z_cc_license_type: 
prism.startingPage: 1090
viewport: width=device-width,initial-scale=1.0,maximum-scale=2.5,user-scalable=yes
twitter:description: Content-aware image restoration (CARE) uses deep learning to improve microscopy images. CARE bypasses the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.
dc.rightsAgent: journalpermissions@springernature.com
prism.section: OriginalPaper
dc.identifier: doi:10.1038/s41592-018-0216-7
dc.subject: Image processing
og:url: https://www.nature.com/articles/s41592-018-0216-7
prism.copyright: 2018 The Author(s), under exclusive licence to Springer Nature America, Inc.