Cell segmentation in live-cell microscopy with deep learning

Live-cell imaging plays a pivotal role in revealing cellular morphology and dynamics, providing critical insights into fundamental biological mechanisms and disease pathogenesis. Image segmentation techniques hold great promise for enabling the quantitative analysis of cellular features, measuring morphological phenomena at the single-cell level. Although traditional segmentation methods are computationally efficient, they often struggle with complex biological data. Advances in deep learning underpin a collection of algorithms with an impressive ability to interpret image content, providing new perspectives on challenges such as cellular heterogeneity, diverse imaging modalities, and real-time analysis in live-cell segmentation. Looking ahead, we discuss key directions that include improving model generalizability across biological systems, advancing three-dimensional volumetric segmentation, and prioritizing user-friendly design. These advancements have deepened our understanding of pathology and facilitated the development of therapeutic strategies, emphasizing the transformative impact of computational innovation on biomedical research and diagnostics.