期刊
NATURE METHODS
卷 11, 期 6, 页码 645-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.2929
关键词
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资金
- MPI-CBG in P.T.'s lab
- Howard Hughes Medical Institute (HHMI) in E.M.'s lab
- Human Frontier Science Program (HFSP) in R.H.S.'s lab [LT000783/2012]
- US National Institutes of Health (NIH) [GM57071]
- HHMI in P.J. Keller's lab
- MPI-CBG
- NIH [GM057071, EB013571, NS083085]
- HHMI
- European Research Council Community [260746]
- HFSP [RGY0093/2012]
- Bundesministerium fur Bildung und Forschung [031A099]
- European Research Council (ERC) [260746] Funding Source: European Research Council (ERC)
Light-sheet fluorescence microscopy is able to image large specimens with high resolution by capturing the samples from multiple angles. Multiview deconvolution can substantially improve the resolution and contrast of the images, but its application has been limited owing to the large size of the data sets. Here we present a Bayesian-based derivation of multiview deconvolution that drastically improves the convergence time, and we provide a fast implementation using graphics hardware.
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