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Cell tracking using phase‐adaptive shape prior
Author(s) -
LAW Y.N.
Publication year - 2013
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12078
Subject(s) - robustness (evolution) , segmentation , computer science , tracking (education) , population , component (thermodynamics) , artificial intelligence , transformation (genetics) , pattern recognition (psychology) , computer vision , frame (networking) , phase (matter) , biological system , data mining , psychology , telecommunications , pedagogy , biochemistry , chemistry , physics , demography , organic chemistry , sociology , biology , gene , thermodynamics
Summary 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.