Efficient Bayesian-based multiview deconvolution View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-06

AUTHORS

Stephan Preibisch, Fernando Amat, Evangelia Stamataki, Mihail Sarov, Robert H Singer, Eugene Myers, Pavel Tomancak

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. More... »

PAGES

645-648

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth.2929

DOI

http://dx.doi.org/10.1038/nmeth.2929

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040897172

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24747812


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