Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Cell segmentation is a cornerstone of biological image analysis, enabling downstream applications such as tracking, counting, and phenotypic profiling. Deep learning has markedly improved segmentation accuracy, leading to broad adoption in research and clinical workflows. However, most current approaches operate on 2D images, neglecting spatial information in the third dimension. This omission is critical, as cellular morphology, migration, and division are inherently three‐dimensional processes. Modern microscopy now offers high‐throughput volumetric imaging, presenting new opportunities for methods that fully exploit 3D data. This review surveys segmentation strategies that incorporate depth information, extending beyond conventional 2D analysis. It distinguishes between pseudo‐3D (2.5D) methods, which balance computational efficiency with limited volumetric context, and fully 3D methods that process volumetric input and output directly. In total, 31 beyond‐2D segmentation approaches are examined and compared, alongside 32 curated volumetric datasets annotated with key metadata such as resolution, size, and ground‐truth availability. Furthermore, a unified reference dataset, compiled from high‐quality open‐source resources, is presented to promote standardized data access, metadata harmonization, and future community‐driven benchmarking efforts in 3D cell segmentation.