Tracking variable number of multiple subcellular structures in 3D

With the introduction of sensitive and fast electronic imaging devices and the development of biological methods to tag proteins of interest by green fluorescent proteins (GFP), it has now become critical to develop automatic quantitative data analysis tools to study the live cell dynamics at subcellular level. In this paper, a sequential Monte Carlo (SMC) method to track variable number of multiple 3D subcellular structures is proposed. First, multiple subcellular structures are represented by a joint state. Then the distribution of the dimension changing joint state is sampled efficiently by the reverse jump Markov chain Monte Carlo (RJMCMC) method designed with update move, identity switch move, disappearing move, and appearing move. The experimental results show that the proposed method can successfully track multiple 3D subcellular structures with different motion modalities such as object appearing and disappearing.