Facilitating data association in particle tracking using autoencoding and score matching

A crucial aspect of automated particle tracking in time-lapse fluorescence microscopy images is the linking or association of detected objects between frames. Recent evaluation studies have shown that the best results are achieved by making use of accurate motion models of the underlying particle dynamics. However, existing approaches often employ rather simple motion models which may be inappropriate for a given application, and even if complex models are used they all require careful user-parameter tuning. To alleviate these problems we propose a novel method based on autoencoding and score matching which can learn the dynamics from the data. Results on both synthetic and real data show the method performs comparable to state-of-the-art linking methods.

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