Section: New Results

Conditional random fields for vesicle traffic analysis with background estimation

Participants : Thierry Pécot, Patrick Bouthemy, Charles Kervrann.

Image analysis applied to fluorescence live cell microscopy has become a key tool in molecular biology since it enables to characterize biological processes in space and time at the subcellular level. In fluorescence microscopy imaging, the moving tagged structures of interest, such as vesicles, appear as bright spots over a static or non-static background. In this work, we consider the problem of vesicle segmentation and time-varying background estimation at the cellular scale. The main idea is to formulate the joint segmentation-estimation problem in the general Conditional Random Field (CRF) framework. Furthermore, segmentation of vesicles and background estimation are alternatively performed by energy minimization using a min cut-max flow algorithm. The proposed approach relies on a detection measure computed from intensity contrasts between neighboring patches in fluorescence microscopy images. We have demonstrated the competitiveness of the proposed method through an experimental comparison with state-of-the-art methods in fluorescence videomicroscopy, for single cell studies. We have also characterized the density of Rab6 transport carriers spatially dispersed at the cell periphery, for two different specific adhesion geometries.

Partners: Jean Salamero, J. Boulanger (UMR 144, PICT IBiSA, CNRS-Institut Curie)