Attention Mechanisms in U-Net Variants for Brain MRI Segmentation: A Narrative Review of Spatial, Channel, and Transformer-Based Approaches

Accurately delineating brain tumors in magnetic resonance imaging (MRI) scans is a critical step in clinical diagnostics, radiotherapy planning, and evaluating therapeutic outcomes. However, several obstacles, including low image contrast, irregular tumor boundaries, and intensity inhomogeneities, often complicate this task. Deep learning methodologies, especially the U-Net architecture, have become prominent tools for automating medical image segmentation. Yet, the traditional U-Net model is limited by its inability to selectively highlight important features or to incorporate extensive contextual information, which constrains its performance in complex imaging scenarios. To overcome these drawbacks, researchers have introduced various attention mechanisms into the U-Net framework, thereby improving its capability to focus dynamically on pertinent spatial and semantic features. This review offers a critical analysis of four notable U-Net variants enhanced with attention modules: Attention U-Net, which integrates gated spatial attention; SE U-Net, employing channel-specific feature recalibration; CBAM-UNet, which combines channel and spatial attention in sequence; and TransUNet, which incorporates Transformer-based self-attention to model long-range dependencies. We comprehensively examine the architectural designs, strategies for integrating attention, segmentation accuracy, interpretability, and computational efficiency of these models. Furthermore, we address prevailing challenges such as limited generalizability, sensitivity to noise, and issues related to clinical adoption. Prospective advancements are also discussed, including hybrid attention strategies, the development of explainable AI, and the integration of multimodal data. This review provides a focused synthesis of key attention-augmented U-Net models for brain tumor segmentation, highlighting their architectural innovations, comparative performance, and practical considerations for clinical use.

Keywords:

Brain tumor , U-net variants , Attention Mechanisms , Deep Learning , MRI Segmentation , Medical Image Analysis