Section: Research Program

Statistics and algorithms for computational microscopy

Many live-cell fluorescence imaging experiments are limited in time to prevent phototoxicity and photobleaching. The amount of light and time required to observe entire cell divisions can generate biological artifacts. In order to produce images compatible with the dynamic processes in living cells as seen in video-microscopy, we study the potential of denoising, superresolution, tracking, and motion analysis methods in the Bayesian and the robust statistics framework to extract information and to improve image resolution while preserving cell integrity.

In this area, we have already demonstrated that image denoising allows images to be taken more frequently or over a longer period of time [5] . The major advantage is to preserve cell integrity over time since spatio-temporal information can be restored using computational methods [8] , [2] , [9] , [4] . This idea has been successfully applied to wide-field, spinning-disk confocal microscopy [1] , TIRF [40] , fast live imaging and 3D-PALM using the OMX system in collaboration with J. Sedat and M. Gustafsson at UCSF [5] . The corresponding ND-safir denoiser software (see Section 6.5 ) has been licensed to a large set of laboratories over the world. New information restoration and image denoising methods are currently investigated to make SIM imaging compatible with the imaging of molecular dynamics in live cells. Unlike other optical sub-diffraction limited techniques (e.g. STED [51] , PALM [41] ) SIM has the strong advantage of versatility when considering the photo-physical properties of the fluorescent probes [48] . Such developments are also required to be compatible with “high-throughput microscopy” since several hundreds of cells are observed at the same time and the exposure times are typically reduced.