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Weigert, M.* ; Schmidt, U.* ; Boothe, T.* ; Müller, A. ; Dibrov, A.* ; Jain, A.* ; Wilhelm, B.* ; Schmidt, D.* ; Broaddus, C.* ; Culley, S.* ; Rocha-Martins, M.* ; Segovia-Miranda, F.* ; Norden, C.* ; Henriques, R.* ; Zerial, M.* ; Solimena, M. ; Rink, J.* ; Tomancak, P.* ; Royer, L.* ; Jug, F.* ; Myers, E.W.*

Content-aware image restoration: Pushing the limits of fluorescence microscopy.

Nat. Methods 15, 1090-1097 (2018)
Postprint DOI
Open Access Green
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Deep; Resolution; Segmentation; Algorithm; Tracking; Reconstruction; Minimization; Embryos
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Journal Nature Methods
Quellenangaben Volume: 15, Issue: 12, Pages: 1090-1097 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Pancreatic Islet Research (IPI)