RJMCMC-based tracking of vesicles in fluorescence time-lapse microscopy

Vesicles are a key component for the transport of materials throughout the cell. To manually analyze the behaviors of vesicles in fluorescence time-lapse microscopy images would be almost impossible. This is also true for the identification of key events, such as merging and splitting. In order to automate and increase the reliability of this processes we introduce a Reversible Jump Markov chain Monte Carlo method for tracking vesicles and identifying merging/splitting events, based on object interactions. We evaluate our method on a series of synthetic videos with varying degrees of noise. We show that our method compares well with other state-of-the-art techniques and well-known microscopy tracking tools. The robustness of our method is also demonstrated on real microscopy videos.