An expectation maximization based method for subcellular particle tracking using multi-angle TIRF microscopy.

Multi-angle total internal reflection fluorescence microscopy (MA-TIRFM) is a new generation of TIRF microscopy to study cellular processes near dorsal cell membrane in 4 dimensions (3D+t). To perform quantitative analysis using MA-TIRFM, it is necessary to track subcellular particles in these processes. In this paper, we propose a method based on a MAP framework for automatic particle tracking and apply it to track clathrin coated pits (CCPs). The expectation maximization (EM) algorithm is employed to solve the MAP problem. To provide the initial estimations for the EM algorithm, we develop a forward filter based on the most probable trajectory (MPT) filter. Multiple linear models are used to model particle dynamics. For CCP tracking, we use two linear models to describe constrained Brownian motion and fluorophore variation according to CCP properties. The tracking method is evaluated on synthetic data and results show that it has high accuracy. The result on real data confirmed by human expert cell biologists is also presented.