Efficient Bayesian-based multiview deconvolution.

Preibisch, Stephan; Amat, Fernando; Stamataki, Evangelia; Sarov, Mihail; Singer, Robert H.; Myers, Gene; Tomancak, Pavel

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Efficient Bayesian-based multiview deconvolution.

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Preibisch, Stephan
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Stamataki, Evangelia
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Sarov, Mihail
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

Singer, Robert H.
Max Planck Society;

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Myers, Gene
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Tomancak, Pavel
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Zitation

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.

Zitierlink: https://hdl.handle.net/21.11116/0000-0001-056E-F

Zusammenfassung

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.