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SUMMARY:Computational aspects of bio-image analysis with focus on lightshe
 et microscopy - Leila Muresan - PDN\, University of Cambridge
DTSTART:20240523T120000Z
DTEND:20240523T130000Z
UID:TALK212893@talks.cam.ac.uk
CONTACT:Jack Atkinson
DESCRIPTION:This work aims to illustrate the impact of high-performance co
 mputing on bioimaging and bioimage analysis. The focus is on a data rich t
 echnique\, lightsheet imaging (also called Selective Plane Illumination Mi
 croscopy or SPIM. Due to its advantages such as fast imaging of large volu
 mes and low phototoxicity\, the role of the technique e.g. in developmenta
 l biology or fast calcium indicator imaging cannot be overstated. However\
 , the blocking factor for light sheet microscopy to reach its full potenti
 al is a computational one: typical datasets consist of time sequences of m
 ulti-tile\, multi-angle\, multi-colour 3D data stacks totalling terabytes 
 of data that need complex processing.\nThe processing steps we focus on ca
 n be categorized in pre-processing steps (denoising\, deskewing\, destripi
 ng\, registration\, stitching\, deconvolution) and downstream analysis. Su
 bsequent image analysis tasks are typically segmentation and tracking\, fo
 llowed by aggregating results across experiments (e.g. atlas creation). We
  discuss a deconvolution algorithm based on a new space varying image form
 ation model\, that would have been computationally prohibitive even on hig
 h-end workstations.
LOCATION:West Hub\, West 1
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