Sequential Monte Carlo Methods for Tracking and Motion Analysis in Bioimaging

Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. We show that probabilistic tracking algorithms, based on Bayesian estimation, offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. We propose a set Bayesian motion analysis techniques, implemented by means of Sequential Monte Carlo methods, for tracking of multiple subresolution objects in fluorescence microscopy image sequences as well as tracking of cells during embryogenesis, and motion analysis in cardiac tagged MRI. Experiments on synthetic as well as real image data from several biological and clinical applications clearly demonstrate the superiority of the algorithms compared to previous solutions.