KEYWORDS: Particles, Signal to noise ratio, Detection and tracking algorithms, Image processing, Microscopy, Algorithm development, Motion models, Point spread functions
With the development of super-resolution fluorescence microscopy, complex dynamic processes in living cells can be observed and recorded with unprecedented temporal and spatial resolution. Single particle tracking is the most important step 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 dynamics in cell, traditional tracking methods have poor performance when dealing with intersecting trajectories. This can be attributed to two main reasons: 1) They do not have point compensation process for overlapping points; 2) They use inefficient motion prediction models. In this paper, we presented a novel Fan-shaped Tracker (FsT) algorithm to reconstruct the trajectories of subcellular molecules in living cells. We proposed a customized point compensation method for overlapping points based on the fan-shape motion trend of the particles to solve the merging trajectory problem. Furthermore, we compared the performance of our Fan-shaped Tracker with five state-of-the-art tracking algorithms in simulated time-lapse movies with variable imaging quality. Our results showed that the Fan-shaped Tracker achieves better performance than other reported methods as we systematically evaluated using a set of standard evaluation parameters. We anticipate that our FsT method will have vast applications in tracking of moving objects in cell.
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