Analyzing dynamic cellular morphology in time-lapsed images enabled by cellular deformation pattern recognition

Computational analysis of cellular morphology aims to provide quantitative information of the global organizational and physiological state of cells, and has long been a major topic of biomedical research. Instead of analyzing morphology of static cells, we concentrate on live-cell deformation in a period of time. According to our observation of dynamic cell behavior, we have assumed that the pattern of cellular deformation is relevant to the cellular state. Moreover, based on our assumption an innovative approach for characterizing the deformation pattern is described and applied into cell classification. After normalizing and aligning cell image sequences, we extract the continuity of deformation at each angle through time-lapse. Then the deformation pattern is given by the histogram of the continuity of deformation. Experimental results demonstrate that the cellular deformation pattern provided by our approach can be applied to discriminate cellular activation. In addition, the deformation pattern recognition makes remarkable progress in the classification of cells.