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Abstract

The cellular morphology and sub-cellular spatial structure critically
influence the function of microbial cells. Similarly, the spatial
arrangement of genotypes and phenotypes in microbial communities has
important consequences for cooperation, competition, and community
functions. Fluorescence microscopy techniques are widely used to measure
spatial structure inside living cells and communities, which often
results in large numbers of images that are difficult or impossible to
analyze manually. The rapidly evolving progress in computational image
analysis has recently enabled the quantification of a large number of
properties of single cells and communities, based on traditional
analysis techniques and convolutional neural networks. Here, we provide
a brief introduction to core concepts of automated image processing,
recent software tools, and how to validate image analysis results. We
also discuss recent advances in image analysis of microbial cells and
communities, and how these advances open up opportunities for
quantitative studies of spatiotemporal processes in microbiology, based
on image cytometry and adaptive microscope control.