[1]朱刚,李四海.融合小波变换的多尺度注意力医学图像分割网络[J].中国医学物理学杂志,2026,43(5):607-613.[doi:DOI:10.3969/j.issn.1005-202X.2026.05.007]
　ZHU Gang,LI Sihai.Multi-scale attention medical image segmentation network fused with wavelet transform[J].Chinese Journal of Medical Physics,2026,43(5):607-613.[doi:DOI:10.3969/j.issn.1005-202X.2026.05.007]
点击复制

融合小波变换的多尺度注意力医学图像分割网络()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第5期
页码:
607-613
栏目:
医学影像物理
出版日期:
2026-05-28

文章信息/Info

Title:
Multi-scale attention medical image segmentation network fused with wavelet transform
文章编号:
1005-202X（2026）05-0607-07
作者:
朱刚; 李四海
甘肃中医药大学医学信息工程学院， 甘肃 兰州 730000
Author(s):
ZHU Gang;  LI Sihai
School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China
关键词:
医学图像分割; 编码器-解码器结构; 离散小波变换; 多尺度注意力
Keywords:
Keywords: medical image segmentation encoder-decoder structure discrete wavelet transform multi-scale attention
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2026.05.007
文献标志码:
A
摘要:
针对图像分割模型中编码器与解码器之间的语义鸿沟，以及图像在传播的过程中会受到噪声的干扰，导致精准分割难以实现的问题，提出一种WMDUNet新型网络模型，使用离散小波变换对编码器中的特征图进行下采样，提取低频特征分量并去除高频特征分量以消除噪声。并采用多尺度双重注意力来增强跳跃连接，弥补编码器与解码器之间的语义鸿沟，实现高精准的医学图像分割。该方法在ACDC和PROMIS12数据集上有3个评价指标达到最优，骰子相似系数均值分别为91.46%和85.85%，交并比均值分别为87.64%和75.22%，豪斯多夫距离均值分别为1.22和3.51 mm，证明该方法的有效性。
Abstract:
Abstract: The semantic gap between the encoder and the decoder in image segmentation models, along with noise interference during feature propagation, restricts the segmentation accuracy. To address this issue, a WMDUNet model is constructed, which adopts discrete wavelet transform to downsample feature maps in the encoder, extracting low-frequency feature components and removing high-frequency feature components for noise elimination. Additionally, multi-scale dual attention is used to enhance the skip connection, bridge the semantic gap between encoders and decoder, and enable high-precision medical image segmentation. On the ACDC and PROMIS12 datasets, this method achieves the best results in 3 evaluation metrics, with mean Dice similarity coefficients of 91.46% and 85.85%, mean intersection over union of 87.64% and 75.22%, and mean Hausdorff distance of 1.22 and 3.51 mm, respectively, which verifies its effectiveness.

相似文献/References:

[1]万俊,聂生东,王远军,等.基于MRI的脑肿瘤分割技术研究进展[J].中国医学物理学杂志,2013,30(04):4266.[doi:10.3969/j.issn.1005-202X.2013.04.013]
[2]林江,戴齐,欧阳婷雪,等.一种边界和马尔可夫随机场相结合的脑MRI医学图像分割方法[J].中国医学物理学杂志,2015,32(05):717.[doi:doi:10.3969/j.issn.1005-202X.2015.05.023]
　[J].Chinese Journal of Medical Physics,2015,32(5):717.[doi:doi:10.3969/j.issn.1005-202X.2015.05.023]
[3]吴迪,胡胜,刘伟峰,等. 基于特征融合视觉显著性的医学图像分割[J].中国医学物理学杂志,2018,35(6):670.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.010]
　WU Di,HU Sheng,LIU Weifeng,et al. Medical image segmentation based on visual saliency of feature fusion[J].Chinese Journal of Medical Physics,2018,35(5):670.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.010]
[4]刘国才,官文静,田娟秀,等. 集成自适应回归核的肿瘤生物靶区随机游走勾画方法[J].中国医学物理学杂志,2018,35(7):758.[doi:DOI:10.3969/j.issn.1005-202X.2018.07.004]
　LIU Guocai,GUAN Wenjing,TIAN Juanxiu,et al. A random walk method with adaptive regression-kernel for delineation of biological target volumes of tumors[J].Chinese Journal of Medical Physics,2018,35(5):758.[doi:DOI:10.3969/j.issn.1005-202X.2018.07.004]
[5]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(5):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[6]张富利,杨安宁,路娜,等.基于FC_DenseNet深度学习网络自动分割肺癌放疗中的危及器官[J].中国医学物理学杂志,2021,38(2):259.[doi:DOI:10.3969/j.issn.1005-202X.2021.02.024]
　ZHANG Fuli,YANG Anning,LU Na,et al.FC_Densenet-based autosegmentation of organs-at-risk in lung cancer radiotherapy[J].Chinese Journal of Medical Physics,2021,38(5):259.[doi:DOI:10.3969/j.issn.1005-202X.2021.02.024]
[7]方新林,方艳红,王迪.基于多模态特征融合的脑瘤图像分割方法[J].中国医学物理学杂志,2022,39(6):682.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.005]
　FANG Xinlin,FANG Yanhong,WANG Di.Brain tumor image segmentation method based on multi-modal feature fusion[J].Chinese Journal of Medical Physics,2022,39(5):682.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.005]
[8]魏坤,沈记全,赵艳梅.MAUNet：用于皮肤病变分割的轻量级模型[J].中国医学物理学杂志,2023,40(5):555.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.006]
　WEI Kun,SHEN Jiquan,ZHAO Yanmei.MAUNet: a lightweight model for skin lesion segmentation[J].Chinese Journal of Medical Physics,2023,40(5):555.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.006]
[9]周金治,胡震,郭莉莉,等.基于GAN-DAUnet的肝脏CT图像肿瘤分割算法[J].中国医学物理学杂志,2023,40(8):971.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.008]
　ZHOU Jinzhi,HU Zhen,et al.Liver CT image tumor segmentation algorithm based on GAN-DAUnet[J].Chinese Journal of Medical Physics,2023,40(5):971.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.008]
[10]黄仟甲,张恒,李奇轩,等.医学图像分割的研究进展[J].中国医学物理学杂志,2024,41(8):939.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.003]
　HUANG Qianjia,ZHANG Heng,,et al.Review on medical image segmentation methods[J].Chinese Journal of Medical Physics,2024,41(5):939.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.003]

备注/Memo

备注/Memo:
【收稿日期】2025-12-22 【基金项目】甘肃省自然科学基金（21JR1RA272，22JR5RA606）；甘肃省教育厅高校教师创新基金（2023B-105） 【作者简介】朱刚，硕士研究生，研究方向：医学图像处理，E-mail: 975578408@qq.com 【通信作者】李四海，教授，研究方向：机器学习、深度学习、医学数据融合，E-mail: lshroom@163.com
更新日期/Last Update: 2026-05-29