MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation

DING Caifu; YANG Chen; JI Qiulang; WANG Yang; ZHANG Bing

doi:10.19304/J.ISSN1000-7180.2021.0950

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MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation

更新时间：2026-09-04

- MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation
- Vol. 39, Issue 3, Pages: 71-77(2022)
- 作者机构：
  
  贵州大学大数据与信息工程学院,贵州,贵阳,550025
- 作者简介：
- 基金信息：
- DOI：10.19304/J.ISSN1000-7180.2021.0950
  CLC：
- Published：2022
- 稿件说明：
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丁才富, 杨晨, 纪秋浪, 王阳, 张兵. MCA-Net：多尺度综合注意力CNN在医学图像分割中的应用[J]. 微电子学与计算机, 2022, 39(3): 71-77.

DING Caifu, YANG Chen, JI Qiulang, et al. MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation[J]. 2022, 39(3): 71-77.
丁才富, 杨晨, 纪秋浪, 王阳, 张兵. MCA-Net：多尺度综合注意力CNN在医学图像分割中的应用[J]. 微电子学与计算机, 2022, 39(3): 71-77. DOI： 10.19304/J.ISSN1000-7180.2021.0950.

DING Caifu, YANG Chen, JI Qiulang, et al. MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation[J]. 2022, 39(3): 71-77. DOI： 10.19304/J.ISSN1000-7180.2021.0950.

摘要

医学图像自动分割技术具有辅助临床医学诊断的功能.为改善CNN模型在医学图像分割中存在感受野小及细节特征不敏感等问题，基于多尺度策略以及注意力机制，提出一种多尺度综合注意力的U形网络架构，以提升医学图像分割质量.首先，提出一个新的双路径因式分解多尺度融合块，以扩展图像特征的感受野，进一步提取图像特征的细节信息.其次，在架构中融入通道和空间融合自注意力块，利用注意力机制的特性，抑制不相关的部分或背景以突显深层特征的空间信息.最后，引入多尺度注意力块.该模块通过融合多个尺度的特征信息，以突出不同尺度中最显著的特征图来适应当前分割对象的大小.为验证模型的可靠性，将所提出的网络模型应用于肺部、细胞轮廓及肝脏等医学图像分割任务.实验结果表明，所提方法在准确率、Dice系数、AUC及灵敏度等评估指标上均优于目前用于医学图像分割的主流方法.

Abstract

The automatic segmentation technology of medical images has the function of assisting clinical medical diagnosis. In order to improve the CNN model in medical image segmentation

such as small receptive fields and insensitive detail features

a multi-scale integrated attention U-shaped network architecture is proposed to improve medical image segmentation quality

which is based on multi-scale strategies and attention mechanisms. First

a new dual-path factorization multi-scale fusion block is proposed to expand the receptive field of image features and further extract the detailed information of image features. Secondly

integrate channel and space fusion self-attention blocks into the architecture

and use the characteristics of the attention mechanism to suppress irrelevant parts or background to highlight the spatial information of deep features. Finally

a multi-scale attention block is introduced. The module fuses feature information of multiple scales to highlight the most prominent feature maps in different scales to adapt to the size of the current segmentation object. In order to verify the reliability of the model

the proposed network model is applied to medical image segmentation tasks such as lungs

cell contours

and liver. Experimental results show that the proposed method is superior to the current mainstream methods for medical image segmentation in terms of accuracy

Dice coefficient

AUC

and sensitivity. It is the mainstream method currently used for medical image segmentation.

关键词

Keywords

references

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