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

Item Type:Article
Title:Content-aware image restoration: pushing the limits of fluorescence microscopy
Creators Name:Weigert, M. and Schmidt, U. and Boothe, T. and Müller, A. and Dibrov, A. and Jain, A. and Wilhelm, B. and Schmidt, D. and Broaddus, C. and Culley, S. and Rocha-Martins, M. and Segovia-Miranda, F. and Norden, C. and Henriques, R. and Zerial, M. and Solimena, M. and Rink, J. and Tomancak, P. and Royer, L. and Jug, F. and Myers, E.W.
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.
Keywords:Image Processing, Machine Learning, Microscopy, Software, Animals, Zebrafish
Source:Nature Methods
Publisher:Nature Publishing Group
Page Range:1090-1097
Date:December 2018
Additional Information:Copyright © The Author(s), under exclusive licence to Springer Nature America, Inc. 2018
Official Publication:https://doi.org/10.1038/s41592-018-0216-7
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