title: Cell tracking using phase-adaptive shape prior.
abstract: 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.
authors: Imaging Informatics Division, Bioinformatics Institute, A*STAR, Singapore.
authors: Y N
authors: YN
authors: Law
mesh_terms: D000465:Algorithms; D002453:Cell Cycle; D045744:Cell Line, Tumor; D058948:Cell Tracking / Q000379:methods*; D049452:Green Fluorescent Proteins; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D049452:Green Fluorescent Proteins
keywords: Bioimage segmentation; cell tracking; microscopy images