Automatic tracking of protein vesicles

TL;DR

This paper presents a novel dynamic programming approach for automatic tracking of protein vesicles in live cell imaging, incorporating Kalman filters to improve accuracy in multi-object scenarios with crossing tracks.

Contribution

It introduces a dynamic programming method for single object tracking and extends it to multi-object tracking with Kalman filters, addressing crossing track issues.

Findings

High accuracy in simulation data for single object tracking

Robustness to noise and parameter variations

Significant improvement in multi-object tracking accuracy

Abstract

With the advance of fluorescence imaging technologies, recently cell biologists are able to record the movement of protein vesicles within a living cell. Automatic tracking of the movements of these vesicles become key for qualitative analysis of dynamics of theses vesicles. In this thesis, we formulate such tracking problem as video object tracking problem, and design a dynamic programming method for tracking single object. Our experiments on simulation data show that the method can identify a track with high accuracy which is robust to the choose of tracking parameters and presence of high level noise. We then extend this method to the tracking multiple objects using the track elimination strategy. In multiple object tracking, the above approach often fails to correctly identify a track when two tracks cross. We solve this problem by incorporating the Kalman filter into the dynamic programming framework. Our experiments on simulated data show that the tracking accuracy is significantly improved.