A comprehensive review of U-Net architectures for medical image segmentation: emerging trends and federated learning perspectives

Abstract U-Net has become a fundamental method in medical image segmentation with its architecture evolving to tackle complex segmentation tasks across modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans and microscopic images. 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.