MSS-UNet : Mamba-Based Multi-directional Selective Scanning for Medical Image Segmentation

Medical image segmentation is a critical task in the field of computer vision, aimed at segmenting specific organs or lesions from input medical images. In current research, multiple deep learning architectures including Convolutional Neural Networks (CNNs) and Transformers are widely employed to tackle this challenge. Nevertheless, CNN-based methods remain constrained in establishing long-range dependencies, whereas Transformer architectures, despite demonstrating superior modeling capacities, are hindered by quadratic computational complexity. To overcome these dual limitations, this study introduces MSS-UNet, a novel architecture integrating State-Space Models (SSMs) that achieves linear computational scaling while maintaining robust long-range modeling capabilities, thereby effectively mitigating the respective shortcomings of CNN and Transformer approaches. We use the U-Net architecture as the overall framework and stack four MSS blocks in the encoder part to capture broad contextual information. In the skip connection part, we introduce the DCA module to enhance the fusion of low-level and high-level features. Extensive experiments were conducted on public benchmark datasets including ISIC2017 and ISIC2018. The experimental results demonstrate that the proposed MSSUNet achieves strong performance in skin lesion segmentation tasks, exhibiting promising effectiveness in medical image analysis.The codes are available at https://github.com/mmm587/MSS-UNet