With the development of super-resolution fluorescence microscopy, complex dynamic processes in living cells can beobserved and recorded with unprecedented temporal and spatial resolution. Single particle tracking is the most importantstep to explore the relationship between the spatio-temporal dynamics of subcellular molecules and their functions.Although previous studies have developed single particle tracking algorithms to quantitatively analyze particle dynamicsin cell, traditional tracking methods have poor performance when dealing with intersecting trajectories. This can beattributed to two main reasons: 1) They do not have point compensation process for overlapping points; 2) They useinefficient motion prediction models. In this paper, we presented a novel Fan-shaped Tracker (FsT) algorithm toreconstruct the trajectories of subcellular molecules in living cells. We proposed a customized point compensationmethod for overlapping points based on the fan-shape motion trend of the particles to solve the merging trajectoryproblem. Furthermore, we compared the performance of our Fan-shaped Tracker with five state-of-the-art trackingalgorithms in simulated time-lapse movies with variable imaging quality. Our results showed that the Fan-shapedTracker achieves better performance than other reported methods as we systematically evaluated using a set of standardevaluation parameters. We anticipate that our FsT method will have vast applications in tracking of moving objects incell.
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