[1]陈梦飞,王娆芬,王海玲,等.基于多尺度卷积与并行反向注意力的医学图像分割[J].中国医学物理学杂志,2025,42(1):27-36.[doi:DOI:10.3969/j.issn.1005-202X.2025.01.005]
　CHEN Mengfei,WANG Raofen,WANG Hailing,et al.Medical image segmentation based on multi-scale convolution and parallel reverse attention[J].Chinese Journal of Medical Physics,2025,42(1):27-36.[doi:DOI:10.3969/j.issn.1005-202X.2025.01.005]
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基于多尺度卷积与并行反向注意力的医学图像分割()
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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1期
页码:
27-36
栏目:
医学影像物理
出版日期:
2025-01-19

文章信息/Info

Title:
Medical image segmentation based on multi-scale convolution and parallel reverse attention
文章编号:
1005-202X（2025）01-0027-10
作者:
陈梦飞1; 王娆芬1; 王海玲1; 李朋2; 宫晓梅2
1.上海工程技术大学电子电气工程学院， 上海 201620； 2.同济大学附属上海市肺科医院放疗科， 上海 200433
Author(s):
CHEN Mengfei1;  WANG Raofen1;  WANG Hailing1;  LI Peng2;  GONG Xiaomei2
1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Radiotherapy, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai 200433, China
关键词:
医学图像分割; 多尺度卷积; 多层感知机; 部分解码器; 反向注意力模块
Keywords:
Keywords: medical image segmentation multi-scale convolution multi-layer perceptron partial decoder reverse attention module
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2025.01.005
文献标志码:
A
摘要:
提出一种基于多尺度卷积与并行反向注意力的医学图像分割网络（RPR-MLP）。在编码器阶段，采用Res2net模块和标记化多层感知机模块作为骨干结构，以提取多尺度信息并增强语义特征的多样性。与此同时，通过并行的部分解码器提高解码器中提取语义信息的准确性。此外，反向注意力模块再次强调对重要区域的关注，进一步提高分割结果的精确性。本文提出的网络在Kvasir和ISIC 2018两个公共数据集上的Dice相似系数（DSC）分别为0.896 7、0.876 2，证明本文网络对医学图像分割的有效性，同时具有较强的泛化能力。将该方法应用于肺肿瘤CT图像LungCancer数据集，评价指标DSC、IoU和F1分别为0.727 8、58.83%和67.85%，其结果与基准网络UNeXt和普通CNN网络U-Net、AttU-Net、U-Net++、PraNet相比，DSC、IoU和F1提升幅度分别为0.030 1~0.057 8、3.16%~4.70%和6.72%~18.53%，结果表明本文提出的网络性能明显优于对比方法。本研究证明RPR-MLP在不同数据集上的有效性和泛化能力，为肺肿瘤图像分割提供重要的技术支持。
Abstract:
Abstract: A medical image segmentation network (RPR-MLP) based on multi-scale convolution and parallel reverse attention is presented. In the encoder, Res2net modules and tokenized multi-layer perceptron modules are used as the backbone structure to extract multi-scale information and enhance the diversity of semantic features. Meanwhile, the accuracy of semantic information extraction in the decoder is improved through parallel partial decoder. Additionally, reverse attention module re-emphasizes the focus on important regions for further improving the accuracy of segmentation results. The proposed method achieves Dice scores of 0.896 7 and 0.876 2 on the Kvasir and ISIC 2018 public datasets, respectively, demonstrating its effectiveness and generalization ability in medical image segmentation. Furthermore, when applied to the lung tumor CT image dataset (LungCancer dataset) collected in the study, the proposed network has Dice score, IoU and F1 score of 0.727 8, 58.83% and 67.85%, respectively, outperforming baseline network (UNeXt) and common CNN (U-Net, AttU-Net, U-Net++ and PraNet) by 0.030 1-0.057 8、3.16%-4.70%, and 6.72%-18.53%, respectively. The study confirms the effectiveness and generalization ability of RPR-MLP network on different datasets, providing important technical support for lung tumor image segmentation.

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备注/Memo

备注/Memo:
【收稿日期】2024-08-21 【基金项目】国家自然科学基金（61803255, 62001284）；上海市科委科技创新行动计划（20Y11913600）；申康三年行动计划肺科培育项目（SKPY2021006） 【作者简介】陈梦飞，硕士研究生，研究方向：医学图像处理，E-mail: 841239459@qq.com 【通信作者】王娆芬，博士，副教授，研究方向：医学图像处理、脑机接口，E-mail: rfwangsues@163.com
更新日期/Last Update: 2025-01-19