SAU-Net：基于U-Net和自注意力机制的医学图像分割方法

张淑军; 彭中; 李辉

doi:10.12263/DZXB.20200984

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SAU-Net：基于U-Net和自注意力机制的医学图像分割方法

学术论文 | 更新时间：2025-12-08

- SAU-Net：基于U-Net和自注意力机制的医学图像分割方法
- SAU-Net: Medical Image Segmentation Method Based on U-Net and Self-Attention
- 电子学报 2022年50卷第10期页码：2433-2442
- 作者机构：
  
  青岛科技大学信息科学技术学院，山东青岛 266061
- 作者简介：
  
  [ "张淑军女，1980年10月出生，山东泰安人.现为青岛科技大学信息科学技术学院副教授.主要研究方向为计算机视觉、人工智能、虚拟现实技术等.E-mail: zhangsj@qust.edu.cn" ]
  [ "彭中男，1994年12月出生，河南南阳人.现为青岛科技大学信息科学技术学院在读硕士研究生.主要研究方向为计算机视觉.E-mail: 964451354@qq.com" ]
  [ "李辉男，1984年3月出生，河南平顶山人.现为青岛科技大学信息科学技术学院副教授.主要研究方向为计算机视觉、人工智能等.E-mail: lipeilin1984xyz@163.com" ]
- 基金信息：
  
  山东省高等学校青创人才引育计划“人工智能与医学影像分析创新团队”建设项目
- DOI：10.12263/DZXB.20200984
  中图分类号： TP301.6;
- 收稿：2020-09-06，
  
  修回：2021-12-04，
  
  纸质出版：2022-10-25
- 稿件说明：
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张淑军,彭中,李辉.SAU-Net：基于U-Net和自注意力机制的医学图像分割方法[J].电子学报,2022,50(10):2433-2442.

ZHANG Shu-jun,PENG Zhong,LI Hui.SAU-Net: Medical Image Segmentation Method Based on U-Net and Self-Attention[J].ACTA ELECTRONICA SINICA,2022,50(10):2433-2442.
张淑军,彭中,李辉.SAU-Net：基于U-Net和自注意力机制的医学图像分割方法[J].电子学报,2022,50(10):2433-2442. DOI： 10.12263/DZXB.20200984.

ZHANG Shu-jun,PENG Zhong,LI Hui.SAU-Net: Medical Image Segmentation Method Based on U-Net and Self-Attention[J].ACTA ELECTRONICA SINICA,2022,50(10):2433-2442. DOI： 10.12263/DZXB.20200984.

摘要

基于深度学习的生物医学图像分割由于其精度的提高，可以更好地辅助医生做精确的诊断.目前主流的基于U-Net的分割模型通过多层卷积进行局部特征的提取，缺失了全局信息，使分割过于局部化而产生误差.本文通过自注意力机制和分解卷积策略对U-Net模型进行改进，提出一种新的深度分割网络SAU-Net，使用自注意力模块增加全局信息，将原U-Net中的级联结构改为逐像素相加，减小维度，降低计算量；提出一种快速简洁的分解卷积方法，将传统卷积分解为两路一维卷积，并加入残差连接强化上下文信息.在BRATS和Kaggle两个脑肿瘤数据集上进行的实验结果表明，SAU-Net在参数量和Dice系数上都有更优的性能.

Abstract

Biomedical image segmentation based on deep learning can better help doctors make an accurate diagnosis due to its enhanced accuracy. At present

the U-Net-based mainstream segmentation model extracts local features through multi-layer convolutions

which lacks global information and leads to over-localized results with errors. This paper improves the U-Net model through the self-attention mechanism and decomposition convolution and proposes a new deep segmentation network called SAU-Net. The model uses the self-attention module to increase global information

and changes the cascade structure in the original U-Net to pixel-by-pixel addition in order to reduce the dimension and cut down the calculation cost. A fast and concise decomposition convolution method is proposed which integrates the traditional convolution into a two-way one-dimensional convolution

and the residual connection is added to enhance the context information. The experimental results conducted on the two brain tumor datasets of BRATS and Kaggle show that SAU-Net has better performance in terms of parameters and the Dice coefficients.

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references

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