[1]袁金丽,李博华,陈沐萱,等.MFMANet：一种融合多尺度特征的多重注意力医学图像分割网络[J].中国医学物理学杂志,2025,42(2):190-198.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.008]
　YUAN Jinli,LI Bohua,CHEN Muxuan,et al.MFMANet: a multi-attention medical image segmentation network fused with multi-scale features[J].Chinese Journal of Medical Physics,2025,42(2):190-198.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.008]
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MFMANet：一种融合多尺度特征的多重注意力医学图像分割网络()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第2期
页码:
190-198
栏目:
医学影像物理
出版日期:
2025-01-20

文章信息/Info

Title:
MFMANet: a multi-attention medical image segmentation network fused with multi-scale features
文章编号:
1005-202X（2025）02-0190-09
作者:
袁金丽; 李博华; 陈沐萱; 蒋仁鼎; JUI SHANAZ SHARMIN; 郭志涛
河北工业大学电子信息工程学院， 天津 300401
Author(s):
YUAN Jinli;  LI Bohua;  CHEN Muxuan;  JIANG Rending;  JUI SHANAZ SHARMIN;  GUO Zhitao
School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, China
关键词:
医学图像分割; 多尺度信息融合; 注意力机制
Keywords:
Keywords: medical image segmentation multi-scale information fusion attention mechanism
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2025.02.008
文献标志码:
A
摘要:
医学图像分割研究在推动高效且精确的自动化图像处理技术方面具有重要意义。然而，为解决医学图像中存在的器官组织形状差异大、边界模糊等导致图像分割结果不准确的问题，提出一种MFMANet新型网络，该网络以“U”型架构为基础，并集成了多尺度信息融合模块和多重注意力模块。具体而言，多尺度信息融合模块通过捕捉网络浅层中的多尺度信息，以弥补编码器和解码器特征之间的语义差距，从而提升了网络应对器官尺寸差异大问题的能力。同时，网络使用多重注意力方法，利用Swin Transformer作为网络深层编解码器，采用通道、空间注意力取代传统的跳跃连接，进而实现了特征信息的更精细提取，以应对边界模糊问题。通过在ACDC和Synapase这两个公共数据集上进行实验，结果显示，与MTUNet方法相比，该方法在骰子相似系数这一关键指标上取得了1.51%和1.29%的显著提升，充分证明了该方法在提高分割网络准确性方面的有效性。
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 networks 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.

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备注/Memo

备注/Memo:
【收稿日期】2024-10-11 【基金项目】河北省教育厅重点项目（ZD2022115） 【作者简介】袁金丽，博士，副教授，研究方向：智能信息处理、计算机视觉、机器学习，E-mail: jinli_yuan@hebut.edu.cn 【通信作者】郭志涛，博士，教授，研究方向：射频识别、嵌入式系统、图像处理，E-mail: 2002089@hebut.edu.cn
更新日期/Last Update: 2025-01-22