A conditional random field model for tracking in densely packed cell structures

Automated tracking of plant and animal cells in time lapse live-imaging datasets of developing multicellular tissues 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 CRFs and spatio-temporal cell to cell correspondences are obtained by performing inference on this CRF using loopy belief propagation. We present results on a (3D+t) confocal image stack of Arabidopsis shoot meristem and show that the method can handle 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.