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Content-aware image restoration: pushing the limits of fluorescence microscopy.
- Source :
-
Nature methods [Nat Methods] 2018 Dec; Vol. 15 (12), pp. 1090-1097. Date of Electronic Publication: 2018 Nov 26. - Publication Year :
- 2018
-
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.
- Subjects :
- Animals
Drosophila melanogaster metabolism
Drosophila melanogaster ultrastructure
HeLa Cells
Humans
Liver metabolism
Liver ultrastructure
Photons
Planarians metabolism
Planarians ultrastructure
Retina metabolism
Retina ultrastructure
Tribolium metabolism
Tribolium ultrastructure
Zebrafish metabolism
Fluorescent Dyes chemistry
Image Processing, Computer-Assisted methods
Microscopy, Fluorescence methods
Software
Subjects
Details
- Language :
- English
- ISSN :
- 1548-7105
- Volume :
- 15
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- Nature methods
- Publication Type :
- Academic Journal
- Accession number :
- 30478326
- Full Text :
- https://doi.org/10.1038/s41592-018-0216-7