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Abstract

Bacteria often grow into matrix-encased three-dimensional (3D) biofilm
communities, which can be imaged at cellular resolution using confocal
microscopy. From these 3D images, measurements of single-cell properties
with high spatiotemporal resolution are required to investigate cellular
heterogeneity and dynamical processes inside biofilms. However, the
required measurements rely on the automated segmentation of bacterial
cells in 3D images, which is a technical challenge. To improve the
accuracy of single-cell segmentation in 3D biofilms, we first evaluated
recent classical and deep learning segmentation algorithms. We then
extended StarDist, a state-of-the-art deep learning algorithm, by
optimizing the post-processing for bacteria, which resulted in the most
accurate segmentation results for biofilms among all investigated
algorithms. To generate the large 3D training dataset required for deep
learning, we developed an iterative process of automated segmentation
followed by semi-manual correction, resulting in >18,000 annotated
Vibrio cholerae cells in 3D images. We demonstrate that this large
training dataset and the neural network with optimized post-processing
yield accurate segmentation results for biofilms of different species
and on biofilm images from different microscopes. Finally, we used the
accurate single-cell segmentation results to track cell lineages in
biofilms and to perform spatiotemporal measurements of single-cell
growth rates during biofilm development.