Tracking Cells and their Lineages via Labeled Random Finite Sets

TL;DR

This paper introduces novel online algorithms for tracking cells and their lineages in live-cell videos by modeling the cell ensemble as labeled random finite sets, enabling accurate lineage resolution and cell tracking.

Contribution

It develops a new spawning model and analytic filters for jointly tracking and resolving cell lineages in time-lapse data, advancing the state of the art.

Findings

Effective in simulation, synthetic, and real data

Accurately resolves cell lineages and trajectories

Demonstrates robustness to cell appearance changes

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.