[1]PENG Chenyang,HE Lifeng,WANG Mengxi,et al.SDA U-Mamba: spectral-domain dynamic fusion and bipolar routing attention for medical image segmentation[J].CAAI Transactions on Intelligent Systems,2026,21(1):284-294.[doi:10.11992/tis.202508032]
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SDA U-Mamba: spectral-domain dynamic fusion and bipolar routing attention for medical image segmentation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 284-294 Column: 人工智能院长论坛 Public date: 2026-03-05
Title:
SDA U-Mamba: spectral-domain dynamic fusion and bipolar routing attention for medical image segmentation
Author(s):
PENG Chenyang1; 2;  HE Lifeng1;  WANG Mengxi1; 2;  DU Xiaogang1; 2;  WANG Yingbo1; 2;  LU Yan3;  LEI Tao1; 2
1. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China;
2. Shaanxi Provincial Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China;
3. School of Light Industry Science and Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
Keywords:
Mamba; medical image segmentation; state space model; spectral-dynamic feature fusion; Fourier transform; attention mechanism; U-Net architecture; multi-scale feature modeling.
CLC:
TP391.4
DOI:
10.11992/tis.202508032
Abstract:
State space models(SSM) represented by Mamba have demonstrated broad application prospects in medical image segmentation owing to their excellent long-range dependency modeling and low linear computational complexity. However, the pixel-wise flattening operation used in these methods destroys the spatial structure of images, leading to insufficient extraction of local details. Additionally, they lack a mechanism to focus on organs and lesions, making them prone to introducing redundant background information in medical images with complex contexts. To address these issues,this study proposes spectral dynamic attention U-Mamba(SDA U-Mamba), a medical image segmentation network that integrates spectral-domain dynamic features with attention mechanisms. The network adopts a hierarchical U-shaped architecture, optimizing Mamba in two aspects: spatial continuity modeling and regional focusing capability. Specifically, a Mamba Spatial-Frequency Attention module is introduced into the network’s shallow layers. This module enhances the model’s local information perception and multi-scale context modeling performance by fusing spatial convolution, frequency-domain transformation, and a pyramid self-attention structure. Furthermore, a Bipolar Routing Attention module is introduced in the deep layers, strengthening the network’s representation of organs and lesions in medical images through dynamic routing selection and sparse activation mechanisms. Experimental results show that SDA U-Mamba achieved significantly better segmentation performance than current state-of-the-art methods across three public medical datasets (BUSI, CVC-ClinicDB, and CHAOS-Liver), with an average improvement in intersection over union(IoU) of 2.61%. The results indicate that the proposed algorithm can be applied to clinical medical image segmentation.
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