Tracking of virus particles in time-lapse fluorescence microscopy image sequences

Abstract

Modern developments in time-lapse microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore the spatio-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking. A quantitative comparison based on synthetic image sequences was carried out to evaluate the performance of the different algorithms. We have also applied the algorithms to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth obtained from manual tracking. It turns out that the probabilistic approach outperforms the deterministic schemes.