Automatic particle detection, localization, and physical parameter estimation via combined expectation maximization and deep learning

Abstract

Single particle tracking is a popular tool that is widely used for studying the dynamic mechanism of nanometer-scale biomolecules. Previously, we proposed a general Expectation Maximization - based framework for simultaneous localization refinement and parameter estimation. Our approach significantly outperforms other methods in terms of localization and parameter estimation, particularly at low signal intensities. The input for this framework is a sequence of images pre-processed from the raw image so in which an individual particle has been isolated and the data linked to form the desired image sequence.