TY - JOUR
T1 - Content-aware image restoration
T2 - pushing the limits of fluorescence microscopy
AU - Weigert, Martin
AU - Schmidt, Uwe
AU - Boothe, Tobias
AU - Müller, Andreas
AU - Dibrov, Alexandr
AU - Jain, Akanksha
AU - Wilhelm, Benjamin
AU - Schmidt, Deborah
AU - Broaddus, Coleman
AU - Culley, Siân
AU - Rocha-Martins, Mauricio
AU - Segovia-Miranda, Fabián
AU - Norden, Caren
AU - Henriques, Ricardo
AU - Zerial, Marino
AU - Solimena, Michele
AU - Rink, Jochen
AU - Tomancak, Pavel
AU - Royer, Loic
AU - Jug, Florian
AU - Myers, Eugene W.
N1 - Funding Information:
The authors thank P. Keller (Janelia) who provided Drosophila data. We thank S. Eaton (MPI-CBG), F. Gruber and R. Piscitello for sharing their expertise in fly imaging and providing fly lines. We thank A. Sönmetz for cell culture work. We thank M. Matejcic (MPI-CBG) for generating and sharing the LAP2b transgenic line Tg(bactin:eGFP-LAP2b). We thank B. Lombardot from the Scientific Computing Facility (MPI-CBG) for technical support. We thank the following Services and Facilities of the MPI-CBG for their support: Computer Department, Light Microscopy Facility and Fish Facility. This work was supported by the German Federal Ministry of Research and Education (BMBF) under the codes 031L0102 (de.NBI) and 031L0044 (Sysbio II) and the Deutsche Forschungsgemeinschaft (DFG) under the code JU 3110/1-1. M.S. was supported by the German Center for Diabetes Research (DZD e.V.). T.B. was supported by an ELBE postdoctoral fellowship and an Add-on Fellowship for Interdisciplinary Life Sciences awarded by the Joachim Herz Stiftung. R.H. and S.C. were supported by the following grants: UK BBSRC (grant nos. BB/M022374/1, BB/P027431/1, and BB/R000697/1), UK MRC (grant no. MR/K015826/1) and Wellcome Trust (grant no. 203276/Z/16/Z).
Publisher Copyright:
© 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85057342134&partnerID=8YFLogxK
U2 - 10.1038/s41592-018-0216-7
DO - 10.1038/s41592-018-0216-7
M3 - Article
C2 - 30478326
AN - SCOPUS:85057342134
SN - 1548-7091
VL - 15
SP - 1090
EP - 1097
JO - NATURE METHODS
JF - NATURE METHODS
IS - 12
ER -