[1]郭乙轩,管江南,武杰,等.融合多尺度特征和注意力机制的肺炎病灶分割方法[J].中国医学物理学杂志,2026,43(7):939-945.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.015]
　GUO Yixuan,GUAN Jiangnan,WU Jie,et al.Pneumonia lesion segmentation method integrating multi-scale feature fusion and attention mechanisms[J].Chinese Journal of Medical Physics,2026,43(7):939-945.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.015]
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融合多尺度特征和注意力机制的肺炎病灶分割方法()

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

卷:
43卷
期数:
2026年第7期
页码:
939-945
栏目:
医学影像物理
出版日期:
2026-07-22

文章信息/Info

Title:
Pneumonia lesion segmentation method integrating multi-scale feature fusion and attention mechanisms
文章编号:
1005-202X（2026）07-0939-07
作者:
郭乙轩1; 管江南1; 武杰1; 姚旭峰2
1.上海理工大学健康科学与工程学院， 上海 200093；2.上海健康医学院医学影像学院， 上海 201318
Author(s):
GUO Yixuan1;  GUAN Jiangnan1;  WU Jie1;  YAO Xufeng2
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
关键词:
肺炎病灶; 图像分割; 特征融合; 多尺度; 注意力机制; 医学图像处理
Keywords:
pneumonia lesion image segmentation feature fusion multi-scale mixed attention medical image processing
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2026.07.015
文献标志码:
A
摘要:
针对肺炎病灶区域大小形状差异大、区域边界不清晰等问题，提出一种融合多尺度特征和注意力机制的分割模型，该模型由多尺度视觉状态空间块构成编码器与解码器的主要结构，利用级联的不同大小的卷积核捕捉不同病灶的多尺度上下文信息，有效应对肺炎病灶形状大小的显著变异性。同时，在跳跃连接层中引入CBAM模块，通过空间和通道维度的双分支交互机制，增强编码器与解码器特征的语义一致性，减少特征融合时的信息丢失。在多个公开肺部病理图像数据集上进行实验，在Large COVID-19 CT scan slice与CNCB两个肺炎病灶分割数据集上的Dice相似系数分别达到87.7%与85.1%，IOU分别达到79.7%与75.3%，并在LUNA16肺结节数据集上验证模型的泛化能力，实验结果表明，相较于传统U-Net架构与transformer架构该模型分割结果具有显著提升。
Abstract:
Abstract: A segmentation model integrating multi-scale features and attention mechanisms is developed to address the challenges of large variations in the size and shape of pneumonia lesions as well as unclear boundaries. The model employs multi-scale visual state space blocks as the main components of both the encoder and decoder. It utilizes cascaded convolution kernels of different sizes to capture multi-scale contextual information from varying-sized lesions, thereby effectively coping with the significant variations in the shape and scale of pneumonia lesions. Meanwhile, a convolutional block attention module is embedded in the skip connection layers. Through a dual-branch interaction mechanism across spatial and channel dimensions, the semantic consistency of encoder-decoder features is improved, and the information loss during feature fusion is alleviated. Experiments are conducted on several public lung pathological image datasets. On the Large COVID-19 CT scan slice dataset and the CNCB pneumonia lesion segmentation dataset, the proposed model achieves Dice similarity coefficients of 87.7% and 85.1%, and IoU of 79.7% and 75.3%, respectively. In addition, the generalization performance of the model is verified on the LUNA16 lung nodule dataset. Experimental results demonstrate that the proposed model exhibits superior segmentation performance over conventional U-Net architectures and Transformer-based models.

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备注/Memo

备注/Memo:
【收稿日期】2026-03-15 【基金项目】上海市科学技术委员会地方院校能力建设项目（23010502700） 【作者简介】郭乙轩，硕士研究生，研究方向：医学图像处理与分析，E-mail: 1670188964@qq.com 【通信作者】武杰，博士，讲师，研究方向：医学图像处理与分析，E-mail: wujie3773@sina.com
更新日期/Last Update: 2026-07-22