001005228 001__ 1005228
001005228 005__ 20240226075416.0
001005228 0247_ $$2doi$$a10.48550/ARXIV.2210.11441
001005228 037__ $$aFZJ-2023-01376
001005228 1001_ $$aRuzaeva, Karina$$b0
001005228 245__ $$aCell tracking for live-cell microscopy using an activity-prioritized assignment strategy
001005228 260__ $$barXiv$$c2022
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001005228 3367_ $$2ORCID$$aWORKING_PAPER
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001005228 3367_ $$2BibTeX$$aARTICLE
001005228 3367_ $$2DataCite$$aOutput Types/Working Paper
001005228 520__ $$aCell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates. Unlike in common multiple object tracking, in microbial live-cell experiments cells are growing, moving, and dividing over time, to form cell colonies that are densely packed in mono-layer structures. With increasing cell numbers, following the precise cell-cell associations correctly over many generations becomes more and more challenging, due to the massively increasing number of possible associations. To tackle this challenge, we propose a fast parameter-free cell tracking approach, which consists of activity-prioritized nearest neighbor assignment of growing cells and a combinatorial solver that assigns splitting mother cells to their daughters. As input for the tracking, Omnipose is utilized for instance segmentation. Unlike conventional nearest-neighbor-based tracking approaches, the assignment steps of our proposed method are based on a Gaussian activity-based metric, predicting the cell-specific migration probability, thereby limiting the number of erroneous assignments. In addition to being a building block for cell tracking, the proposed activity map is a standalone tracking-free metric for indicating cell activity. Finally, we perform a quantitative analysis of the tracking accuracy for different frame rates, to inform life scientists about a suitable (in terms of tracking performance) choice of the frame rate for their cultivation experiments, when cell tracks are the desired key outcome.
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001005228 588__ $$aDataset connected to DataCite
001005228 650_7 $$2Other$$aComputer Vision and Pattern Recognition (cs.CV)
001005228 650_7 $$2Other$$aQuantitative Methods (q-bio.QM)
001005228 650_7 $$2Other$$aFOS: Computer and information sciences
001005228 650_7 $$2Other$$aFOS: Biological sciences
001005228 7001_ $$0P:(DE-HGF)0$$aCohrs, Jan-Christopher$$b1
001005228 7001_ $$0P:(DE-Juel1)191491$$aKasahara, Keitaro$$b2$$ufzj
001005228 7001_ $$0P:(DE-Juel1)140195$$aKohlheyer, Dietrich$$b3$$ufzj
001005228 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b4$$ufzj
001005228 7001_ $$0P:(DE-HGF)0$$aBerkels, Benjamin$$b5$$eCorresponding author
001005228 773__ $$a10.48550/ARXIV.2210.11441
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001005228 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2171$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
001005228 9141_ $$y2023
001005228 9201_ $$0I:(DE-Juel1)IBG-1-20101118$$kIBG-1$$lBiotechnologie$$x0
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