  The spread of microbial infections is governed by the self-organization of
bacteria on surfaces. Limitations of live imaging techniques make collective
behaviors in clinically relevant systems challenging to quantify. Here, novel
experimental and image analysis techniques for high-fidelity single-cell
segmentation of bacterial colonies are developed. Machine learning-based
segmentation models are trained solely using synthetic microscopy images that
are processed to look realistic using state-of-the-art image-to-image
translation methods, requiring no biophysical modeling. Accurate single-cell
segmentation is achieved for densely packed single-species colonies and
multi-species colonies of common pathogenic bacteria, even under suboptimal
imaging conditions and for both brightfield and confocal laser scanning
microscopy. The resulting data provide quantitative insights into the
self-organization of bacteria on soft surfaces. Thanks to their high
adaptability and relatively simple implementation, these methods promise to
greatly facilitate quantitative descriptions of bacterial infections in varied
environments.
