A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation

Abstract

Accurate medical image segmentation plays a critical role in clinical diagnosis, treatment planning, and a wide range of healthcare applications. Although U-shaped CNNs and Transformer-based architectures have shown promise, CNNs struggle to capture long-range dependencies, whereas Transformers suffer from quadratic growth in computational cost as image resolution increases. To address these issues, we propose HCMUNet, a novel medical image segmentation model that innovatively combines the local feature extraction capabilities of CNNs with the efficient long-range dependency modeling of Mamba, enhancing feature representation while reducing computational cost. In addition, HCMUNet features a redesigned skip connection and a novel attention module that integrates multi-scale features to recover spatial details lost during down-sampling and to promote richer cross-dimensional interactions. HCMUNet achieves Dice Similarity Coefficients (DSC) of 90.32%, 81.52%, and 92.11% on the ISIC 2018, Synapse multi-organ, and ACDC datasets, respectively, outperforming baseline methods by 0.65%, 1.05%, and 1.39%. Furthermore, HCMUNet consistently outperforms U-Net and Swin-UNet, achieving average Dice score improvements of approximately 5% and 2% across the evaluated datasets. These results collectively affirm the effectiveness and reliability of the proposed model across different segmentation tasks.