Fig. 1

The synthetic image generation process: a Schematic of linear colonies of cells in a microfluidic device known colloquially as the mother machine. b Synthetic image generation pipeline: rigid body physics simulations are combined with agent-based modelling to simulate bacterial growth in the device. These simulations are convolved with the microscope’s point spread function, which is generated using known parameters of the objective lens. This output image is then further optimised to match real images. Scale bar = 1 μm. c Synthetic data can be adapted to different biological conditions, variations in microfluidic designs, and imaging modalities. With real data, many experiments would need to be conducted to generate training data with the same kind of coverage. Scale bar = 1 μm. d Typical timescales for individual steps in the generation of training data. e Humans annotating images had variable performances and consistently undersegmented cells, especially in small stationary phase cells. f SyMBac is approximately 10,000× faster than a human at generating training data (10,000 images in less than 10 min)