Quantitative BioImaging Conference (QBI) 2020.

Quantitative BioImaging Conference (QBI) 2020 Conference at the University of Oxford’s Mathematical Institute, Oxford, UK (January 6-9, 2020). 

[Brief illustration] – Single Particle Tracking (SPT) is a powerful class of tools for acquiring information about the dynamics of biological macromolecules moving inside living cells. In this work, we are interested in comparing algorithms with respect to the performance of localization and motion model estimation at very low signal levels. A standard approach for estimation is to use Gaussian Fitting (GF) to determine the position of an emitter in an image, followed by linking to produce a trajectory. These trajectories are then analyzed by using Mean Square Displacement (MSD) or Maximum Likelihood (ML) to estimate motion parameters, such as diffusion coefficients. However, both MSD and the standard formulation of ML assume a linear measurement model with simple additive Gaussian noise, which begins to fail as image intensity decreases. We have introduced two nonlinear methods that more accurately model camera-based measurements: Sequential Monte Carlo combined with Expectation Maximization (SMC-EM), and Unscented Kalman Filter combined with Expectation Maximization (U-EM).