[1]温帆,杨萍,张鑫,等.基于特征增强的多分支U-Net肺结节分割[J].中国医学物理学杂志,2023,40(11):1343-1349.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.005]
　WEN Fan,YANG Ping,ZHANG Xin,et al.Pulmonary nodule segmentation using multi-branch U-Net based on feature enhancement[J].Chinese Journal of Medical Physics,2023,40(11):1343-1349.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.005]
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基于特征增强的多分支U-Net肺结节分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第11期
页码:
1343-1349
栏目:
医学影像物理
出版日期:
2023-11-24

文章信息/Info

Title:
Pulmonary nodule segmentation using multi-branch U-Net based on feature enhancement
文章编号:
1005-202X（2023）11-1343-07
作者:
温帆; 杨萍; 张鑫; 田吉; 王金华
北京联合大学智慧城市学院， 北京 100101
Author(s):
WEN Fan;  YANG Ping;  ZHANG Xin;  TIAN Ji;  WANG Jinhua
Smart City College, Beijing Union University, Beijing 100101, China
关键词:
肺结节; 3D U-Net; Transformer; 多尺度残差块; 坐标注意力
Keywords:
Keywords: pulmonary nodule 3D U-Net Transformer multi-scale residual block coordinate attention
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2023.11.005
文献标志码:
A
摘要:
针对肺结节尺度差异大、边界纹理不清晰、背景干扰严重导致的肺结节分割不精确的问题，以3D U-Net为基础，引入Transformer结构，设计一种基于特征增强的多分支U-Net肺结节分割算法。Transformer从全局角度提取肺结节及周边组织的结构特征，浅层3D U-Net提取图像纹理特征；利用上述结构特征及纹理特征进行特征增强；多尺度残差块和3D坐标注意力对3D U-Net进行改进，用于提取特征增强后的肺结节多尺度信息，并在3D U-Net解码器基础上，对深层语义信息进行复用，最终实现肺结节分割。在LIDC-IDRI数据集上对该模型进行验证，精确度、敏感度、Dice相似性系数分别达到90.04%、86.64%、88.80%，综合分割性能优于其他算法。
Abstract:
Abstract: To address the problem of the inaccurate segmentation of pulmonary nodules caused by large scale differences, unclear boundary texture and serious background interference, a multi-branch U-Net based on feature enhancement is designed for pulmonary nodules segmentation. The method uses Transformer to extract structural features of pulmonary nodules and surrounding tissues from a global perspective, and shallow 3D U-Net to extract the texture features. The extracted both structural and texture features are used for feature enhancement. In addition, a multi-scale residual block and 3D coordinate attention module are designed to modify 3D U-Net for obtaining multi-scale information of pulmonary nodules with enhanced features. Based on 3D U-Net decoder, the deep semantic information is reused for accomplishing the segmentation of pulmonary nodules. The verification on LIDC-IDRI dataset shows that the proposed model has a precision, sensitivity and Dice similarity coefficient of 90.04%, 86.64% and 88.80%, respectively, exhibiting superior comprehensive segmentation performance as compared with other algorithms.

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备注/Memo

备注/Memo:
【收稿日期】2023-10-26 【基金项目】国家自然科学基金（62172045，62272049） 【作者简介】温帆，硕士研究生，研究方向：医学图像处理，E-mail: 905525170@qq.com? 【通信作者】杨萍，博士，副教授，研究方向：信号与信息处理、医学图像处理，E-mail: xxtyangping@buu.edu.cn
更新日期/Last Update: 2023-11-24