Advances in deep learning for bacterial image segmentation in optical microscopy

Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics, offering critical insights into bacterial physiology and pathogenicity. Image segmentation techniques enable quantitative analysis of bacterial structures, facilitating precise measurement of morphological variations and population behaviors at single-cell resolution. This paper reviews advancements in bacterial image segmentation, emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches. Convolutional neural networks (CNNs), U-Net architectures, and three-dimensional (3D) frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes. These methods combine automated feature extraction with physics-informed postprocessing. Despite progress, challenges persist in computational efficiency, cross-species generalizability, and integration with multimodal experimental workflows. Future progress will depend on improving model robustness across species and imaging modalities, integrating multimodal data for phenotype-function mapping, and developing standard pipelines that link computational tools with clinical diagnostics. These innovations will expand microbial phenotyping beyond structural analysis, enabling deeper insights into bacterial physiology and ecological interactions.