mAIcrobe: an open-source framework for high-throughput bacterial image analysis

Abstract Microscopy of bacterial cells is crucial for studying bacterial growth, cell division, or responses to antibiotics. However, analyzing these images can be challenging because bacteria vary widely in shape and behaviour. Many existing tools require specialised computational expertise, which can limit their accessibility. Here we present mAIcrobe, an open-source image analysis framework that makes advanced bacterial microscopy analysis more accessible by combining deep learning-based segmentation methods, including StarDist, CellPose, and U-Net, with quantitative morphological profiling and a flexible neural network-based classification model. mAIcrobe can analyse a wide range of bacterial species, from spherical Staphylococcus aureus to rod-shaped Escherichia coli , across different microscopy modalities, within a single environment. We demonstrate the utility of mAIcrobe by using it to identify antibiotic-induced changes in E. coli and cell cycle defects in S. aureus DnaA mutants. The framework is designed to be modular and extensible, with Jupyter notebooks provided to facilitate the development of custom models, thereby making AI-driven image analysis more accessible to the microbiology community.