CMUNet: Enhancing Image Segmentation through Advanced Channel Dependency Modeling

Image segmentation aims to partition an image into meaningful regions, facilitating the analysis of its structure and content. U-shaped models inspired by UNet have become the dominant architecture in this field, leveraging an encoder-decoder design with skip connections to retain spatial details. Within this framework, CNNs and Transformers have achieved remarkable success but still suffer from either limited receptive fields or the high computational cost of long-range modeling. Recently, Mamba has emerged as a powerful approach for modeling long-range dependencies with linear complexity. However, existing efforts to integrate Mamba into U-Net primarily emphasize spatial feature extraction, largely overlooking the intricate inter-channel relationships encapsulating diverse semantic patterns. In this paper, we propose Channel Mamba UNet CMUNet , which explicitly captures channel dependencies with two key components: Channel Mamba CMamba , a module that adaptively recalibrates channel-wise features in the encoder, and Skip Connection Mamba SkiM , a mechanism that facilitates multi-level channel fusion to bridge the semantic gap between the encoder and decoder. Comprehensive evaluations on MoNuSeg, GlaS, and ISIC-2018 confirm the effectiveness of CMUNet, achieving Dice scores of 81.28, 92.18, and 91.13, along with superior IoU and HD95 metrics.