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This review offers a comprehensive analysis of various U-Net architectures including U-Net++, Residual U-Net, Dense U-Net and more recent transformer-based models like TransUNet and Swin-UNet. These architectures introduce optimizations like improved feature propagation, gradient flow and self-attention mechanisms, significantly enhancing segmentation accuracy. Despite its widespread success, U-Net faces limitations in handling data imbalance, computational complexity and challenges with multi-modal and large-scale data. The integration of federated learning with U-Net addresses privacy concerns by enabling secure, collaborative model training across healthcare institutions while maintaining data confidentiality. This review also highlights the applications of U-Net variants in key medical imaging tasks, including brain segmentation, retinal vessel segmentation, cell and nuclei segmentation, prostate segmentation and skin lesion segmentation, which play a critical role in early disease detection and treatment planning. In addition to exploring these applications, the paper examines benchmark datasets, loss functions and evaluation metrics essential for assessing U-Net architectures. The review also discusses emerging trends like self-supervised learning, lightweight models for resource-constrained environments and interpretability techniques, offering potential directions for future research. By presenting both the advancements and limitations of U-Net, this review provides valuable insights into how these models can be optimized and applied to real-world medical applications.<\/jats:p>","DOI":"10.1007\/s00521-026-12198-6","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T01:32:06Z","timestamp":1781487126000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A comprehensive review of U-Net architectures for medical image segmentation: emerging trends and federated learning perspectives"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0749-1088","authenticated-orcid":false,"given":"Md. 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