Tracking rapid intracellular movements: A Bayesian random set approach

TL;DR

This paper introduces a Bayesian random set-based algorithm for automated tracking of intracellular organelle movements, improving analysis of noisy microscopy data over manual methods.

Contribution

It develops a novel Bayesian filtering approach using random sets and Gaussian mixtures for multi-object tracking within cells.

Findings

Effective tracking on synthetic data

Successful application to experimental microscopy data

Enhanced analysis of organelle dynamics

Abstract

We focus on the biological problem of tracking organelles as they move through cells. In the past, most intracellular movements were recorded manually, however, the results are too incomplete to capture the full complexity of organelle motions. An automated tracking algorithm promises to provide a complete analysis of noisy microscopy data. In this paper, we adopt statistical techniques from a Bayesian random set point of view. Instead of considering each individual organelle, we examine a random set whose members are the organelle states and we establish a Bayesian filtering algorithm involving such set states. The propagated multi-object densities are approximated using a Gaussian mixture scheme. Our algorithm is applied to synthetic and experimental data.