
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.09.02.610652
preprint
1
Article
Ultrack: pushing the limits of cell tracking across biological scales
Bragantini Jordao http://orcid.org/0000-0001-7652-2735

Theodoro Ilan http://orcid.org/0000-0003-4019-3380

Zhao Xiang http://orcid.org/0000-0002-3339-4806

Huijben Teun A.P.M. http://orcid.org/0000-0002-8984-2882

Hirata-Miyasaki Eduardo http://orcid.org/0000-0002-1016-2447

VijayKumar Shruthi http://orcid.org/0000-0001-6679-1261

Balasubramanian Akilandeswari http://orcid.org/0009-0007-7426-6409

Lao Tiger http://orcid.org/0009-0005-6793-8102

Agrawal Richa
Xiao Sheng http://orcid.org/0000-0001-8959-2388

Lammerding Jan http://orcid.org/0000-0003-4335-8611

Mehta Shalin http://orcid.org/0000-0002-2542-3582

Falcao Alexandre http://orcid.org/0000-0002-2914-5380

Jacobo Adrian http://orcid.org/0000-0001-9381-6292

Lange Merlin http://orcid.org/0000-0003-0534-4374

Royer Loic A. http://orcid.org/0000-0002-9991-9724

03 9 2024
2024.09.02.610652https://creativecommons.org/licenses/by-nc/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.09.02.610652
nihpp-2024.09.02.610652.pdf
Tracking live cells across 2D, 3D, and multi-channel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, achieving accurate cell tracking remains challenging, particularly in complex and crowded tissues where cell segmentation is often ambiguous. We present Ultrack, a versatile and scalable cell-tracking method that tackles this challenge by considering candidate segmentations derived from multiple algorithms and parameter sets. Ultrack employs temporal consistency to select optimal segments, ensuring robust performance even under segmentation uncertainty. We validate our method on diverse datasets, including terabyte-scale developmental time-lapses of zebrafish, fruit fly, and nematode embryos, as well as multi-color and label-free cellular imaging. We show that Ultrack achieves state-of-the-art performance on the Cell Tracking Challenge and demonstrates superior accuracy in tracking densely packed embryonic cells over extended periods. Moreover, we propose an approach to tracking validation via dual-channel sparse labeling that enables high-fidelity ground truth generation, pushing the boundaries of long-term cell tracking assessment. Our method is freely available as a Python package with Fiji and napari plugins and can be deployed in a high-performance computing environment, facilitating widespread adoption by the research community.
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pmc
