SCFMUNet: A fusion architecture based on multi-scale state space model and channel attention for medical image segmentation.

Medical image segmentation is essential for disease diagnosis and therapy planning, but the complexity of multi-organ structures and blurred skin lesion boundaries poses challenges. CNNs and Transformers are constrained by limited receptive fields and high computational complexity. The state-space model effectively captures long-range dependencies with linear complexity but struggles with local modeling and channel attention.These methods struggle to detect subtle differences in lesion areas, leading to poor performance in medical image segmentation, especially when the lesions are discontinuous or boundaries are unclear.To address these challenges, we propose SCFMUNet, which enhances both local and global modeling across multi-scale features and effectively captures spatial and channel semantics. SCFMUNet integrates three key fusion strategies: 1) At the bottleneck, the multi-scale state-space fusion module is designed to combine convolutions and the SS2D method, processes and fuses the encoder stage features. 2) In the skip connections, the gated adaptive channel mechanism dynamically adjusts the encoder features and fuses them with the decoder stage features using channel-wise addition. 3) In the decoder stages, the spatial channel state-space model performs spatial and channel-level modeling on the fused features from the skip connection stage and the previous decoder layer. Experiments on four public datasets were conducted. On the Synapse dataset and ACDC dataset, our SCFMUNet achieved 82.31 % and 92.14 % on Dice. Compared to state-of-the-art methods, SCFMUNet improves Dice by 0.85 % on Synapse and 1.0 % on ACDC. On the ISIC2017 and ISIC2018 skin lesion datasets, SCFMUNet achieved Dice scores of 90.69 % and 89.69 %, with improvements ranging from 0.5 % to 2 % compared to state-of-the-art methods. Experimental results show that SCFMUNet outperforms state-of-the-art methods on four publicly available biomedical datasets.The source code is publicly available https://github.com/zzzeed/SCFMUNet.