<![CDATA[Retinal Vessel Segmentation (RVS) is crucial for diagnosing eye diseases, requiring deep learning models to accurately segment complex vascular structures. While initial architectures like U-Net established the baseline, traditional CNNs struggle with capturing long-range dependencies necessary for full vessel connectivity. This Systematic Literature Review (SLR) critically analyzes the performance and efficiency trade-offs across conventional CNNs, Attention-based U-Net variants, and the novel Mamba/State Space Model (SSM) hybrids. Evaluation is based on segmentation quality (AUC, Sensitivity) and computational cost (Parameters, FLOPs). The findings confirm that advanced Attention-based models successfully boost Sensitivity (SE)—critical for fine vessels—over the baseline. Crucially, the emerging Mamba Hybrids (e.g., CRMA-UNet) offer superior efficiency, capturing global context with linear complexity rather than the quadratic cost of early Transformers. This shift, coupled with the necessity for lightweight designs (e.g., LDMRes-Net at 0.072M parameters), confirms that Mamba/SSM hybrids represent the optimal balance, making them the most promising future direction for efficient clinical deployment]]>
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