Accurate Segmentation of Bacterial Cells using Synthetic Training Data

Abstract We present a novel method of bacterial image segmentation using machine learning based on Synthetic Micro-graphs of Bacteria (SyMBac). SyMBac allows for rapid, automatic creation of arbitrary amounts of training data that combines detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, with access to the ground truth positions of cells. We also demonstrate that models trained on SyMBac data generate more accurate and precise cell masks than those trained on human annotated data, because the model learns the true position of the cell irrespective of imaging artefacts.