Tracking Cells and their Lineages via Labeled Random Finite Sets

Abstract

Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide. This paper proposes novel online algorithms for jointly tracking and resolving lineages of an unknown and time-varying number of cells from time-lapse video data. Our approach involves modeling the cell ensemble as a labeled random finite set with labels representing cell identities and lineages. A spawning model is developed to take into account cell lineages and changes in cell appearance prior to division. We then derive analytic filters to propagate multi-object distributions that contain information on the current cell ensemble including their lineages. We also develop numerical implementations of the resulting multi-object filters. Experiments using simulation, synthetic cell migration video, and real time-lapse sequence, are presented to demonstrate the capability of the solutions.

References in corpus (6)

• Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
• Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
• Multiple Object Tracking in Unknown Backgrounds with Labeled Random Finite Sets
• Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
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Cited by in corpus (3)

• Multi-Scan Multi-Sensor Multi-Object State Estimation
• Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering
• Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories