DMM-UNet: dual-path multi-scale Mamba UNet for medical image segmentation

State space models have shown promise in medical image segmentation by modeling long-range dependencies with linear complexity. However, they are limited in their ability to capture local features, which hinders their capacity to extract multiscale details and integrate global and local contextual information effectively. To address these shortcomings, we propose the dual-path multi-scale Mamba UNet (DMM-UNet) model.

This architecture facilitates deep fusion of local and global features through multi-scale modules within a U-shaped encoder-decoder framework. First, we introduce the multi-scale channel attention selective scanning block in the encoder, which combines global selective scanning with multi-scale channel attention to model both long-range and local dependencies simultaneously. Second, we design the spatial attention selective scanning block for the decoder. This block integrates global scanning with spatial attention mechanisms, enabling precise aggregation of semantic features through gated weighting. Finally, we develop the multi-dimensional collaborative attention layer to extract complementary attention weights across height, width, and channel dimensions, facilitating cross-space-channel feature interactions.

Experiments were conducted on the ISIC17, ISIC18, Synapse, and ACDC datasets. One of the indicators, Dice similarity coefficient, achieved 89.88% on the ISIC17 dataset, 90.52% on the ISIC18 dataset, 83.07% on the Synapse dataset, and 92.60% on the ACDC dataset. There are also other indicators that perform well on this model.

The DMM-UNet model effectively addresses the shortcomings of state space models by enabling the integration of both local and global features, improving segmentation performance, and offering enhanced multiscale feature fusion for medical image segmentation tasks.