MFMANet:a multi-attention medical image segmentation network fused with multi-scale features
10.3969/j.issn.1005-202X.2025.02.008
- VernacularTitle:MFMANet:一种融合多尺度特征的多重注意力医学图像分割网络
- Author:
Jinli YUAN
1
;
Bohua LI
1
;
Muxuan CHEN
1
;
Rending JIANG
1
;
JUI SHANAZ SHARMIN
1
;
Zhitao GUO
1
Author Information
1. 河北工业大学电子信息工程学院,天津 300401
- Publication Type:Journal Article
- Keywords:
medical image segmentation;
multi-scale information fusion;
attention mechanism
- From:
Chinese Journal of Medical Physics
2025;42(2):190-198
- CountryChina
- Language:Chinese
-
Abstract:
The research on medical image segmentation is of great significance in advancing efficient and accurate automated image processing techniques.To address the problem of inaccurate segmentation results caused by significant variations in organ tissue shapes and blurred boundaries present in medical images,a novel network named MFMANet is proposed.Built upon a"U"-shaped architecture,the network integrates multi-scale information fusion modules and multi-attention modules.Specifically,multi-scale information modules capture multi-scale information in the shallow layers of the network to bridge the semantic gap between encoder and decoder features,thereby enhancing the network's ability to handle large variations in organ sizes.Regarding the issue of blurred boundaries,multi-attention mechanism utilizes Swin Transformer as the deep encoder-decoder network,employing channel and spatial attention instead of traditional skip connections to achieve finer feature extraction.Experimental results on the ACDC and Synapse public datasets show that the proposed method achieves improvements of 1.51%and 1.29%in Dice similarity coefficient as compared with MTUNet,fully demonstrating its effectiveness in enhancing segmentation network accuracy.
