@InProceedings{7362895,
  author = {D. Nam and K. Arkill and R. Eales and L. Hodgson and P. Verkade and A. Achim},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {RJMCMC-based tracking of vesicles in fluorescence time-lapse microscopy},
  year = {2015},
  pages = {2801-2805},
  abstract = {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.},
  keywords = {biomedical optical imaging;cellular transport;fluorescence;Markov processes;medical image processing;Monte Carlo methods;object tracking;optical microscopy;RJMCMC-based tracking;fluorescence time-lapse microscopy;material transport;vesicle merging;vesicle splitting;Reversible Jump Markov chain Monte Carlo method;microscopy tracking tools;real microscopy videos;Microscopy;Target tracking;Merging;Proposals;Monte Carlo methods;Videos;Light microscopy;biomedical imaging;MCMC;merging;splitting},
  doi = {10.1109/EUSIPCO.2015.7362895},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104803.pdf},
}

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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. 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