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001047300 005__ 20251023202112.0
001047300 0247_ $$2doi$$a10.48550/ARXIV.2503.21914
001047300 037__ $$aFZJ-2025-04215
001047300 1001_ $$0P:(DE-Juel1)176923$$aSeiffarth, Johannes$$b0$$ufzj
001047300 245__ $$aPyUAT: Open-source Python framework for efficient and scalable cell tracking
001047300 260__ $$barXiv$$c2025
001047300 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1761202234_21072
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001047300 500__ $$aWe acknowledge the inspiring scientific environment provided by the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), thank Axel Theorell for insightful discussions, and Wolfgang Wiechert for continuous support. This work was supported by the President’s Initiative and Networking Funds of the Helmholtz Association of German Research Centres [SATOMI ZT-I-PF-04-011, EMSIG ZT-I-PF-04-44].
001047300 520__ $$aTracking individual cells in live-cell imaging provides fundamental insights, inevitable for studying causes and consequences of phenotypic heterogeneity, responses to changing environmental conditions or stressors. Microbial cell tracking, characterized by stochastic cell movements and frequent cell divisions, remains a challenging task when imaging frame rates must be limited to avoid counterfactual results. A promising way to overcome this limitation is uncertainty-aware tracking (UAT), which uses statistical models, calibrated to empirically observed cell behavior, to predict likely cell associations. We present PyUAT, an efficient and modular Python implementation of UAT for tracking microbial cells in time-lapse imaging. We demonstrate its performance on a large 2D+t data set and investigate the influence of modular biological models and imaging intervals on the tracking performance. The open-source PyUAT software is available at https://github.com/JuBiotech/PyUAT, including example notebooks for immediate use in Google Colab.
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001047300 650_7 $$2Other$$aQuantitative Methods (q-bio.QM)
001047300 650_7 $$2Other$$aComputer Vision and Pattern Recognition (cs.CV)
001047300 650_7 $$2Other$$aFOS: Biological sciences
001047300 650_7 $$2Other$$aFOS: Computer and information sciences
001047300 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b1$$ufzj
001047300 773__ $$a10.48550/ARXIV.2503.21914
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001047300 9141_ $$y2025
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