Fig. 3

Model training, evaluation, and timing benchmarks: a Schematic of the U-net model being trained using synthetic data and then segmenting real data to produce accurate masks. b SyMBac can retrain generalised models, such as Omnipose (a derivative of Cellpose, allowing for mask reconstruction from arbitrary morphologies). Because Omnipose was not trained on any microfluidic device images, it fails to properly segment the image, attaching masks to the mother machine trench geometry (though it admirably segments cells within the trench). Retraining Omnipose with SyMBac’s synthetic data results in near perfect segmentation, with no more trench artefacts. c A typical time to train the network, either Omnipose or DeLTA (on 2000 images) and segment approximately one million images (Nvidia GeForce 1080Ti)