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

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

URL: http://arxiv.org/abs/2210.11441v1
Date: Thu, 20 Oct 2022 17:40:31 GMT
Title: Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy
Authors: Karina Ruzaeva, Jan-Christopher Cohrs, Keitaro Kasahara, Dietrich Kohlheyer, Katharina N\"oh, Benjamin Berkels
Abstract summary: Cell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates. 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. We propose a fast-prioritized cell tracking approach, which consists of activity-prioritized nearest neighbor assignment of growing cells and a solver that assigns splitting mother cells to their daughters.
Score: 0.9134244356393666
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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