Molecule tracking with marker-less stage drift correction in super resolution microscopy

Fluorescence microscopy is a technique which allows the
imaging of intracellular dynamics through the activation of
fluorescent molecules attached to them. It is a very important technique
because it can be used to analyse the behavior of intracellular processes
in vivo in contrast to methods like electron microscopy. There are
several challenges related to the extraction of meaningful data from
images acquired from optical microscopes due to the low levels of
signal to noise ratio and the fact that point-like objects are observed
as blurred spots due to diffraction limit of the optical system. Another
consideration is that for the study of intracellular dynamics, multiple
particles must be tracked at the same time, which is a challenging task due
to problems such as the presence of false positives and missed detections
in the acquired data. Additionally, the objective of the microscope is
not completely static with respect to the cover slip which introduces
bias in the measurements. In this talk, we will discuss a Bayesian approach to
simultaneously track the locations of the particles and the stage drift using
image data obtained from fluorescence microscopy experiments. Namely,
detections are extracted from the acquired frames using image processing
techniques, and then these detections are used to accurately estimate
the particle positions and simultaneously correct the drift introduced by
the motion of the sample stage. A single cluster Probability Hypothesis
Density (PHD) filter with object classification is used for the estimation
of the multiple target state assuming different motion behaviors. The
tracking method is tested and evaluated on live-cell PALM data.