Efficient bayesian-based multiview deconvolution

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

Research output: Contribution to journalArticle

103 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)645-648
Number of pages4
JournalNature Methods
Volume11
Issue number6
DOIs
StatePublished - Jun 2014

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Fingerprint Dive into the research topics of 'Efficient bayesian-based multiview deconvolution'. Together they form a unique fingerprint.

  • Cite this

    Preibisch, S., Amat, F., Stamataki, E., Sarov, M., Singer, R. H., Myers, E., & Tomancak, P. (2014). Efficient bayesian-based multiview deconvolution. Nature Methods, 11(6), 645-648. https://doi.org/10.1038/nmeth.2929