Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy

TL;DR

This paper introduces a fast, parameter-free cell tracking method for live-cell microscopy that uses activity-prioritized assignment and a combinatorial solver, improving accuracy in densely packed, growing cell colonies.

Contribution

It presents a novel activity-based assignment strategy combined with a combinatorial solver, enhancing cell tracking accuracy without requiring parameter tuning.

Findings

Improved tracking accuracy over traditional methods.

Effective in densely packed, growing cell colonies.

Provides a standalone activity metric for cell activity.

Abstract

Cell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates. Unlike in common multiple object tracking, in microbial live-cell experiments cells are growing, moving, and dividing over time, to form cell colonies that are densely packed in mono-layer structures. With increasing cell numbers, following the precise cell-cell associations correctly over many generations becomes more and more challenging, due to the massively increasing number of possible associations. To tackle this challenge, we propose a fast parameter-free cell tracking approach, which consists of activity-prioritized nearest neighbor assignment of growing cells and a combinatorial solver that assigns splitting mother cells to their daughters. As input for the tracking, Omnipose is utilized for instance segmentation. Unlike conventional nearest-neighbor-based tracking approaches, the assignment steps of our proposed method are based on a Gaussian activity-based metric, predicting the cell-specific migration probability, thereby limiting the number of erroneous assignments. In addition to being a building block for cell tracking, the proposed activity map is a standalone tracking-free metric for indicating cell activity. Finally, we perform a quantitative analysis of the tracking accuracy for different frame rates, to inform life scientists about a suitable (in terms of tracking performance) choice of the frame rate for their cultivation experiments, when cell tracks are the desired key outcome.