Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images

• Published in: PloS one Vol. 16; no. 5; p. e0243115
• Main Authors: Lin, Ye, Andersson, Sean B.
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 21.05.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial neural networks; Background noise; Cameras; CMOS; Computer and Information Sciences; Computer simulation; Deep learning; Drafting software; Engineering and Technology; Experimental methods; Image segmentation; Kalman filters; Localization; Machine learning; Macromolecules; Mechanical engineering; Neural networks; Noise; Noise levels; Parameter estimation; Parameter identification; Particle tracking; Particle trajectories; Physical properties; Physical Sciences; Random variables; Research and Analysis Methods; Systems engineering; Trajectory analysis; United States; United States > US

Single Particle Tracking (SPT) is a well known class of tools for studying the dynamics of biological macromolecules moving inside living cells. In this paper, we focus on the problem of localization and parameter estimation given a sequence of segmented images. In the standard paradigm, the locatio...