Accurate Segmentation of Bacterial Cells using Synthetic Training Data

Abstract

We present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac). SyMBac is a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs. The major advantages of our approach are: 1) synthetic training data can be generated virtually instantly, and on demand; 2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; 3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in ones experimental setup no longer requires the laborious process of manually generating new training data for each change. Our benchmarking results 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. Machine-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology.