NETWORK TOMOGRAPHY AND MINIMAL PATHS FOR TRAFFIC FLOWESTIMATION IN MOLECULAR IMAGING

Abstract : Green Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells. Original image analysis methods are then required to process challenging 2D or 3D image sequences. To address the tracking problem of several hundreds of objects, we propose an original framework that provides general information about molecule transport, that is about traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the recent advances in Network Tomography commonly used in network communications. In this paper, we address image partition given vesicle stocking areas and multipaths routing for vesicle transport. This approach has been developed for real image sequences and Rab proteins.
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Communication dans un congrès
ISBI - IEEE International Symposium On Biomedical Imaging : from nano to macro - 2008, May 2008, Paris, France. 2008
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Thierry Pécot, Charles Kervrann. NETWORK TOMOGRAPHY AND MINIMAL PATHS FOR TRAFFIC FLOWESTIMATION IN MOLECULAR IMAGING. ISBI - IEEE International Symposium On Biomedical Imaging : from nano to macro - 2008, May 2008, Paris, France. 2008. 〈hal-00919688〉

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