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

Abstract

Quantitative analysis in bacterial microscopy is often hindered by diverse cell morphologies, population heterogeneity, and the requirement for specialised computational expertise. To address these challenges, mAIcrobe is introduced as an open-source framework that broadens access to advanced bacterial image analysis by integrating a suite of deep learning models. mAIcrobe incorporates multiple segmentation algorithms, including StarDist, CellPose, and U-Net, alongside comprehensive morphological profiling and an adaptable neural network classifier, all within the napari ecosystem. This unified platform enables the analysis of a wide range of bacterial species, from spherical Staphylococcus aureus to rod-shaped Escherichia coli, across various microscopy modalities within a single environment. The biological utility of mAIcrobe is demonstrated through its application to antibiotic phenotyping in E. coli and the identification of cell cycle defects in S. aureus DnaA mutants. The modular design, supported by Jupyter notebooks, facilitates custom model development and extends AI-driven image analysis capabilities to the broader microbiology community. Building upon the foundation established by eHooke, mAIcrobe represents a substantial advancement in automated and reproducible bacterial microscopy.