Cell tracking using phase-adaptive shape prior

Automated tracking of cell population is very crucial for quantitative measurements of dynamic cell-cycle behaviour of individual cells. This problem involves several subproblems and a high accuracy of each step is essential to avoid error propagation. In this paper, we propose a holistic three-component system to tackle this problem. For each phase, we first learn a mean shape as well as a model of the temporal dynamics of transformation, which are used for estimating a shape prior for the cell in the current frame. We then segment the cell using a level set-based shape prior model. Finally, we identify its phase based on the goodness-of-fit of the data to the segmentation model. This phase information is also used for fine-tuning the segmentation result. We evaluate the performance of our method empirically in various aspects and in tracking individual cells from HeLa H2B-GFP cell population. Highly accurate validation results confirm the robustness of our method in many realistic scenarios and the essentiality of each component of our integrating system.