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

Weigert, M., Schmidt, U., Boothe, T., Müller, A., Dibrov, A., Jain, A., et al. (2018). Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature methods, 15(12), 1090-1097. doi:10.1038/s41592-018-0216-7.

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 Creators:
Weigert, Martin1, Author           
Schmidt, Uwe1, Author           
Boothe, Tobias1, Author           
Müller, Andreas, Author
Dibrov, Alexandr1, Author           
Jain, Akanksha1, Author           
Wilhelm, Benjamin, Author
Schmidt, Deborah, Author
Broaddus, Coleman1, Author           
Culley, Sian, Author
Rocha-Martins, Mauricio1, Author           
Segovia-Miranda, Fabián1, Author           
Norden, Caren1, Author           
Henriques, Ricardo, Author
Zerial, Marino1, Author           
Solimena, Michele1, Author           
Rink, Jochen1, Author           
Tomancak, Pavel1, Author           
Royer, Loic1, Author           
Jug, Florian1, Author           
Myers, Eugene W1, Author            more..
Affiliations:
1Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society, ou_2340692              

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 Abstract: 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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 Dates: 2018-12-01
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: DOI: 10.1038/s41592-018-0216-7
Other: cbg-7207
PMID: 30478326
 Degree: -

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Title: Nature methods
  Other : Nat Methods
Source Genre: Journal
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Pages: - Volume / Issue: 15 (12) Sequence Number: - Start / End Page: 1090 - 1097 Identifier: -