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001033892 037__ $$aFZJ-2024-06730
001033892 1001_ $$0P:(DE-Juel1)175101$$aPaul, Richard D.$$b0$$eFirst author$$ufzj
001033892 1112_ $$a2024 IEEE International Symposium on Biomedical Imaging (ISBI)$$cAthens$$d2024-05-27 - 2024-05-30$$wGreece
001033892 245__ $$aRobust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth
001033892 260__ $$bIEEE$$c2024
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001033892 520__ $$aLive-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results.
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001033892 7001_ $$0P:(DE-Juel1)176923$$aSeiffarth, Johannes$$b1$$ufzj
001033892 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b2$$ufzj
001033892 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b3$$eCorresponding author$$ufzj
001033892 773__ $$a10.1109/ISBI56570.2024.10635267
001033892 8564_ $$uhttps://juser.fz-juelich.de/record/1033892/files/Robust_Approximate_Characterization_of_Single-Cell_Heterogeneity_in_Microbial_Growth.pdf$$yRestricted
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001033892 9141_ $$y2024
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