A Medical Image Segmentation Network Based on Cross-Visual State Space and Multi-Branch Interactive Attention

XUE Wei; CHEN Chuang-hui; DU Ming-yang; ZHONG Ping; ZHENG Xiao

doi:10.12263/DZXB.20250642

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A Medical Image Segmentation Network Based on Cross-Visual State Space and Multi-Branch Interactive Attention

PAPERS | 更新时间：2025-12-27

- A Medical Image Segmentation Network Based on Cross-Visual State Space and Multi-Branch Interactive Attention
- ACTA ELECTRONICA SINICA Vol. 53, Issue 9, Pages: 3331-3344(2025)
- 作者机构：
  
  1.安徽工业大学计算机科学与技术学院，安徽马鞍山 243032
  2.国防科技大学电子对抗学院，安徽合肥 230037
  3.国防科技大学电子科学学院，湖南长沙 410073
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62441207);Science and Technology Innovation Tackle Plan Project of Maanshan(2024RGZN001)
- DOI：10.12263/DZXB.20250642
  CLC： TP391.4;
- Received：23 July 2025，
  
  Accepted：22 September 2025，
  
  Published：25 September 2025
- 稿件说明：
移动端阅览
薛伟, 陈创慧, 杜明洋, 等. 基于交叉视觉状态空间与多分支交互注意力的医学图像分割[J]. 电子学报, 2025, 53(09): 3331-3344.

XUE Wei, CHEN Chuang-hui, DU Ming-yang, et al. A Medical Image Segmentation Network Based on Cross-Visual State Space and Multi-Branch Interactive Attention[J]. Acta Electronica Sinica, 2025, 53(09): 3331-3344.
薛伟, 陈创慧, 杜明洋, 等. 基于交叉视觉状态空间与多分支交互注意力的医学图像分割[J]. 电子学报, 2025, 53(09): 3331-3344. DOI：10.12263/DZXB.20250642

XUE Wei, CHEN Chuang-hui, DU Ming-yang, et al. A Medical Image Segmentation Network Based on Cross-Visual State Space and Multi-Branch Interactive Attention[J]. Acta Electronica Sinica, 2025, 53(09): 3331-3344. DOI：10.12263/DZXB.20250642

摘要

医学图像分割是智慧医疗领域的关键技术，旨在精准识别并分割影像中的器官或病变区域，为临床诊断与治疗决策提供可靠的量化依据.近年来，基于卷积神经网络（Convolutional Neural Network，CNN）的医学图像分割方法因其优异的局部特征提取能力得到广泛应用.然而，受限于卷积操作固有的局部感受野，CNN在建模长距离空间依赖和全局上下文信息方面仍存在不足.尽管基于Transformer的方法通过自注意力机制实现了对全局特征的建模，但计算复杂度随序列长度的平方增长，制约了其实际应用效率.针对上述问题，本文提出一种新的医学图像分割网络，该网络包含交叉视觉状态空间（Cross-Vision State Space，C-VSS）和多分支交互注意力（Multi-Branch Interactive Attention，MBIA）两个核心模块.C-VSS模块融合卷积操作的局部感知优势与状态空间的长序列建模能力，通过双分支协作策略，在保持线性计算复杂度的同时，实现对局部和全局特征的有效提取与融合.MBIA模块则通过多分支架构增强多尺度上下文信息的表征能力，并在编码器与解码器之间建立双向信息交互通道，实现跨层特征的动态融合，从而提升模型对复杂结构的感知能力.为验证所提方法的有效性，在CVC-ColonDB、ISIC2017、ISIC2018和COVID-19这4个公开医学图像分割数据集上开展试验.结果表明：与次优方法相比，本文方法在交并比（Intersection over Union，IoU）指标上分别提升了约0.94、0.83、1.04和2.28个百分点，在Dice相似系数（Dice Similarity Coefficient，DSC）指标上分别提升了约0.63、0.50、1.56和1.51个百分点.此外，平均数（Average，Avg）指标在4个数据集上分别达到91.51%、91.74%、91.30%和88.78%，均优于所有对比方法，展现出最优性能，充分验证了所提方法在分割性能上的优越性.进一步开展消融实验以验证核心模块的作用，实验表明：单独移除C-VSS模块后，IoU指标分别下降3.62、2.15、1.69和2.13个百分点，DSC指标分别下降2.25、1.29、1.02和1.40个百分点；单独移除MBIA模块后，IoU指标分别下降10.11、0.50、1.08和1.97个百分点，DSC指标分别下降6.54、0.30、0.65和1.30个百分点.实验结果充分证明C-VSS与MBIA模块的有效性，且MBIA模块对性能提升的贡献更为显著，二者协同作用可进一步优化模型性能.

Abstract

Medical image segmentation is a key technology in the field of smart healthcare

aiming to accurately identify and segment organs or pathological regions within images

thereby providing reliable quantitative evidence for clinical diagnosis and treatment decision-making. In recent years

medical image segmentation methods based on convolutional neural network (CNN) have been widely adopted due to their excellent capability in extracting local features. However

due to the inherent local receptive field of convolution operations

CNN still suffers from limitations in modeling long-range spatial dependencies and global contextual information. Although Transformer-based methods achieve global feature modeling through the self-attention mechanism

their computational complexity grows quadratically with sequence length

limiting their efficiency in practical applications. To mitigate the aforementioned issues

this paper proposes a new medical image segmentation network

which mainly consist of two core modules: cross-vision state space (C-VSS) and multi-branch interactive attention (MBIA). The C-VSS module integrates the local perception advantage of convolutional operation with the long-sequence modeling capability of state space model. Through a dual-branch collaborative strategy

it achieves effective extraction and fusion of local and global features while maintaining linear computational complexity. The MBIA module enhances the representation of multi-scale contextual information through a multi-branch architecture and establishes bidirectional information interaction pathways between the encoder and the decoder to enable dynamic fusion of cross-level features

thereby improving the model’s ability to perceive complex structures. Experimental results on four public medical image segmentation datasets

including CVC-ColonDB

ISIC2017

ISIC2018

and COVID-19

demonstrate that our method outperforms the second-best approach by approximately 0.94

0.83

1.04

and 2.28 percentage points in intersection over union (IoU) and 0.63

0.50

1.56

and 1.51 percentage points in dice similarity coefficient (DSC)

respectively. In addition

the proposed method achieves average (Avg) scores of 91.51%

91.74%

91.30%

and 88.78% on the four datasets

respectively

all of which are higher than those of the comparative methods

demonstrating its superior segmentation performance. Furthermore

ablation studies show that removing the C-VSS module alone leads to a decrease of 3.62

2.15

1.69

and 2.13 percentage points in IoU

and 2.25

1.29

1.02

and 1.40 percentage points in DSC

respectively. Removing the MBIA module alone results in a decline of 10.11

0.50

1.08

and 1.97 percentage points in IoU

and 6.54

0.30

0.65

and 1.30 percentage points in DSC

respectively. The experimental results fully verify the effectiveness of the C-VSS and MBIA modules

indicate that the MBIA module contributes more significantly to performance improvement

and reveal a notable synergy between the two.

关键词

Keywords

references

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