  Live-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.
