Minimal paths and probabilistic models for origin-destination traffic estimation in live cell imaging

International audienceGreen 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 vesicle transport, that is traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the 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 fluorescence image sequences and Rab proteins

Minimal paths and probabilistic models for origin-destination traffic estimation in live cell imaging

International audienceGreen 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 vesicle transport, that is traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the 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 fluorescence image sequences and Rab proteins