[1]李雪,周金治,莫春梅,等.基于特征融合的U-Net肺自动分割方法[J].中国医学物理学杂志,2021,38(6):704-712.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.009]
　LI Xue,ZHOU Jinzhi,et al.U-Net automatic lung segmentation based on feature fusion[J].Chinese Journal of Medical Physics,2021,38(6):704-712.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.009]
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基于特征融合的U-Net肺自动分割方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第6期
页码:
704-712
栏目:
医学影像物理
出版日期:
2021-06-29

文章信息/Info

Title:
U-Net automatic lung segmentation based on feature fusion
文章编号:
1005-202X（2021）06-0704-09
作者:
李雪1; 2; 周金治1; 2; 莫春梅1; 2; 余玺1; 2
1.西南科技大学信息工程学院， 四川 绵阳 621000； 2.特殊环境机器人技术四川省重点实验室， 四川 绵阳 621000
Author(s):
LI Xue1;  2;  ZHOU Jinzhi1;  2;  MO Chunmei1;  2;  YU Xi1;  2
1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621000, China
关键词:
肺实质; U-Net; 自动分割; 颜色特征; 纹理特征; 特征融合
Keywords:
Keywords: lung parenchyma U-Net automatic segmentation color feature texture feature feature fusion
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2021.06.009
文献标志码:
A
摘要:
目的：将肺部颜色特征与纹理特征融合形成一种更有效的特征，并利用改进的U-Net深度学习网络结构对肺部CT影像进行图像分割以准确提取肺实质区域。方法：使用的CT影像数据来源于LIDC-IDRI数据库，首先通过色彩空间转换、高阶邻域统计的方法分别提取颜色特征和纹理特征，然后采用加权平均直方图融合两类特征，最后将特征输入改进后的U-Net模型，进行1 000次CT扫描测试，以达到完整的肺实质输出。结果：该方法最终的骰子系数、灵敏度、特异性分别为93%、96%和97%。结论：本方法较单一特征分割方法具有较高的分割精度，有效提高肺实质的分割精度，可为后续的肺部疾病自动诊断提供可靠基础，减少临床诊断的成本并节省医生诊断时间。
Abstract:
Abstract: Objective To obtain a type of more effective feature by combining lung color features with texture features, and to accurately extract lung parenchyma by segmenting the lung CT image using improved U-Net deep learning network structure. Methods The CT image data used in the study were derived from LIDC-IDRI dataset. The color features and texture features were firstly extracted through color space conversion and high-order neighborhood statistics. Then, the mean weighted histogram was used to fuse the two types of features and the obtained features were input into the improved U-Net model for 1 000 CT scan tests, thereby achieving a complete lung parenchyma output. Results The Dice coefficient, sensitivity and specificity of the proposed method were 93%, 96% and 97%, respectively. Conclusion The proposed method which has a higher segmentation accuracy than the single feature segmentation method can effectively improve the accuracy of lung parenchyma segmentation and provide a reliable basis for the subsequent automatic diagnosis of lung diseases, thus reducing the cost of clinical diagnosis and shortening the time for diagnosis.

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备注/Memo

备注/Memo:
【收稿日期】2020-11-19 【基金项目】国家自然科学基金（11472297）；西南科技大学研究生创新基金（20ycx0056） 【作者简介】李雪，硕士研究生，研究方向：医学图像处理、机器学习，E-mail: 15378336785@163.com 【通信作者】周金治，副教授，硕士生导师，主要研究方向：智能信息处理、计算机网络，E-mail: zhoujinzhi@swust.edu.cn
更新日期/Last Update: 2021-06-29