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  Efficient Bayesian-based multiview deconvolution.

Preibisch, S., Amat, F., Stamataki, E., Sarov, M., Singer, R. H., Myers, G., et al. (2014). Efficient Bayesian-based multiview deconvolution. Nature Methods, 11(6), 645-648.

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 Creators:
Preibisch, Stephan1, Author           
Amat, Fernando, Author
Stamataki, Evangelia1, Author           
Sarov, Mihail1, Author           
Singer, Robert H.2, Author
Myers, Gene1, Author           
Tomancak, Pavel1, Author           
Affiliations:
1Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society, ou_2340692              
2Max Planck Society, ou_persistent13              

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 Abstract: 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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 Dates: 2014
 Publication Status: Issued
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 Identifiers: eDoc: 705642
Other: 5739
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Title: Nature Methods
Source Genre: Journal
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Pages: - Volume / Issue: 11 (6) Sequence Number: - Start / End Page: 645 - 648 Identifier: -