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001     1005228
005     20240226075416.0
024 7 _ |a 10.48550/ARXIV.2210.11441
|2 doi
037 _ _ |a FZJ-2023-01376
100 1 _ |a Ruzaeva, Karina
|b 0
245 _ _ |a Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy
260 _ _ |c 2022
|b arXiv
336 7 _ |a Preprint
|b preprint
|m preprint
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|s 1677848775_4687
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336 7 _ |a WORKING_PAPER
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336 7 _ |a Electronic Article
|0 28
|2 EndNote
336 7 _ |a preprint
|2 DRIVER
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a Output Types/Working Paper
|2 DataCite
520 _ _ |a 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.
536 _ _ |a 2171 - Biological and environmental resources for sustainable use (POF4-217)
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588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Computer Vision and Pattern Recognition (cs.CV)
|2 Other
650 _ 7 |a Quantitative Methods (q-bio.QM)
|2 Other
650 _ 7 |a FOS: Computer and information sciences
|2 Other
650 _ 7 |a FOS: Biological sciences
|2 Other
700 1 _ |a Cohrs, Jan-Christopher
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Kasahara, Keitaro
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700 1 _ |a Kohlheyer, Dietrich
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700 1 _ |a Nöh, Katharina
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700 1 _ |a Berkels, Benjamin
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|e Corresponding author
773 _ _ |a 10.48550/ARXIV.2210.11441
909 C O |o oai:juser.fz-juelich.de:1005228
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
|b Forschungsbereich Erde und Umwelt
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914 1 _ |y 2023
920 1 _ |0 I:(DE-Juel1)IBG-1-20101118
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980 _ _ |a preprint
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IBG-1-20101118
980 _ _ |a UNRESTRICTED

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