Frequency-Enhanced Lightweight Vision Mamba Network for Medical Image Segmentation

Automatic segmentation of medical images is a crucial step for lesion measurement in computer-aided diagnosis. Convolutional neural networks (CNNs) and vision transformers (ViTs) are widely adopted but have limitations. To address these challenges, we propose a frequency-enhanced lightweight vision Mamba (FMamba) network for automatic medical image segmentation. Specifically, we introduce the vision state-space (VSS) and frequency feature enhancement (FFE) modules for efficient parallel feature extraction. The VSS module employs 2-D-Selective-Scan (SS2D) to scan feature maps in multiple directions, effectively building long-range dependencies. At the same time, the FFE module refines the frequency domain of the feature maps, yielding enhanced global feature representations, thereby enhancing global context awareness. Compared to UNet, our method reduces GFLOPs and Parameters by 25.99 times and 5.84 times, respectively. On the BUSI dataset, Dice and intersection over union (IoU) scores improved by 3.25% and 3.35%, respectively. On Dataset B, improvements were 2.69% and 2.21%, respectively. Our method can effectively integrate state-space model (SSM) and frequency domain features, surpassing existing methods in medical image segmentation tasks.