Context aware spatio-temporal cell tracking in densely packed multilayer tissues

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Modern live imaging technique enables us to observe the internal part of a tissue over time by generating serial optical images containing spatio-temporal slices of hundreds of tightly packed cells. Automated tracking of plant and animal cells from such time lapse live-imaging datasets of a developing multicellular tissue is required for quantitative, high throughput analysis of cell division, migration and cell growth. In this paper, we present a novel cell tracking method that exploits the tight spatial topology of neighboring cells in a multicellular field as contextual information and combines it with physical features of individual cells for generating reliable cell lineages. The 2D image slices of multicellular tissues are modeled as a conditional random field and pairwise cell to cell similarities are obtained by estimating marginal probability distributions through loopy belief propagation on this CRF. These similarity scores are further used in a spatio-temporal graph labeling problem to obtain the optimal and feasible set of correspondences between individual cell slices across the 4D image dataset. We present results on (3D + t) confocal image stacks of Arabidopsis shoot meristem and show that the method is capable of handling many visual analysis challenges associated with such cell tracking problems, viz. poor feature quality of individual cells, low SNR in parts of images, variable number of cells across slices and cell division detection.

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