It is crucial to dynamically analyze the movement of neutrophils (a type of white cell) during the process of inflammation. However, manually tracking and analyzing the cells is a time-consuming task due to the large volume of cells and similar appearance. To facilitate neutrophils analysis and address the issues mentioned above, we propose to leverage high-order temporal information as a cue to track neutrophils. A tensor-based approach is introduced to encode the high-order motion pattern and appearance variation for multi-frame multicell association. To evaluate the proposed method, we collected 354 sequences of cells from 200 frames of microscopic images. We conduct a systematic study on the collected data and show significant performance improvement over other solutions.

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