Efficient Bayesian-based Multi-View Deconvolution

Light sheet fluorescence microscopy is able to image large specimen with high resolution by imaging the sam-ples from multiple angles. Multi-view deconvolution can significantly improve the resolution and contrast of the images, but its application has been limited due to the large size of the datasets. Here we present a Bayesian-based derivation of multi-view deconvolution that drastically improves the convergence time and provide a fast implementation utilizing graphics hardware. MAIN DOCUMENT Modern light sheet microscopes1–3 are able to acquire large, developing specimens with high temporal and spatial resolution typically by imaging them from multiple directions (Fig. 1a). The low photodamage offered by a light sheet microscopes design allows the recording of massive, time-lapse datasets that have the potential to enable the reconstruction of entire lineage trees of the developing specimen. However, accurate segmentation and tracking of nuclei and cells in these datasets remain a challenge because image quality is limited by the optical properties of the imaging system and the compromises between acquisition speed and resolution. De-convolution utilizes knowledge about the optical system to substantially increase spatial resolution and contrast after acquisition. An advantage unique to light sheet microscopy and in particular to Selective Plane Illumina