[1]张倩雯,陈明,秦玉芳,等.基于3D ResUnet网络的肺结节分割[J].中国医学物理学杂志,2019,36(11):1356-1361.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.021]
　ZHANG Qianwen,CHEN Ming,QIN Yufang,et al.Lung nodule segmentation based on 3D ResUnet network[J].Chinese Journal of Medical Physics,2019,36(11):1356-1361.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.021]
点击复制

基于3D ResUnet网络的肺结节分割()
分享到：

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

卷:
36卷
期数:
2019年第11期
页码:
1356-1361
栏目:
其他（激光医学等）
出版日期:
2019-11-25

文章信息/Info

Title:
Lung nodule segmentation based on 3D ResUnet network
文章编号:
1005-202X（2019）11-1356-06
作者:
张倩雯1; 陈明1; 2; 秦玉芳1; 2; 陈希1
1.上海海洋大学信息学院， 上海 201306； 2.农业部渔业信息重点实验室， 上海 201306
Author(s):
ZHANG Qianwen1;  CHEN Ming1;  2;  QIN Yufang1;  2;  CHEN Xi1
1. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China; 2. Key Laboratory of Fisheries Information, Ministry of Agriculture, Shanghai 201306, China
关键词:
肺结节; 分割; 深度残差结构; 召回率; ResUnet
Keywords:
pulmonary nodule;  segmentation;  deep residual structure;  recall rate;  ResUnet
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.11.021
文献标志码:
A
摘要:
目的：将深度残差结构和U-Net网络结合形成新的网络ResUnet，并利用ResUnet深度学习网络结构对胸部CT影像进行图像分割以提取肺结节区域。方法：使用的CT影像数据来源于LUNA16数据集，首先对CT图像预处理提取出肺实质，然后对其截取立体图像块并进行数据增强来扩充样本数，形成相应的肺结节掩膜图像，最后将生成的样本输入到ResUnet模型中进行训练。结果：本研究模型最终的精度和召回率分别为35.02%和97.68%。结论：该模型能自动学习肺结节特征，为后续的肺癌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的时间。 【关键词】肺结节；分割；深度残差结构；召回率；ResUnet
Abstract:
Objective To propose a novel network ResUnet by combining deep residual structure with U-Net network, and to extract lung nodule region by segmenting the chest CT image with the use of ResUnet deep learning network structure. Methods The CT image data used in the study were derived from LUNA16 dataset. The lung parenchyma was firstly extracted from CT image preprocessing, and then, the stereo image block was intercepted and the simple size was expanded by data enhancement, thereby obtaining the corresponding lung nodule mask image. Finally, the obtained simple were imported into ResUnet model for training. Results The final accuracy and recall rate of the proposed model were 35.02% and 97.68%, respectively. Conclusion The proposed model can automatically learn the characteristics of pulmonary nodules and provide a reliable basis for the subsequent automatic diagnosis of lung cancer, thus reducing the cost of clinical diagnosis and shortening the time for diagnosis.

相似文献/References:

[1]张 娟,杨子立,袁克虹.基于改进SymRG算法的腹主动脉瘤分割及自动定位方法[J].中国医学物理学杂志,2015,32(01):60.[doi:10.3969/j.issn.1005-202X.2015.01.015]
[2]王晓飞,聂生东,王远军,等.改进的K-均值聚类算法及其在脑组织分割中的应用[J].中国医学物理学杂志,2014,31(02):4760.[doi:10.3969/j.issn.1005-202X.2014.02.010]
[3]王晓飞,聂生东,王远军.改进的Brain Extraction Tool算法及其在脑实质分割中的应用[J].中国医学物理学杂志,2016,33(2):113.[doi:10.3969/j.issn.1005-202X.2016.02.002]
　[J].Chinese Journal of Medical Physics,2016,33(11):113.[doi:10.3969/j.issn.1005-202X.2016.02.002]
[4]徐效文,杨志刚.光学相干层析医学图像分割研究现状[J].中国医学物理学杂志,2016,33(7):697.[doi:10.3969/j.issn.1005-202X.2016.07.011]
　[J].Chinese Journal of Medical Physics,2016,33(11):697.[doi:10.3969/j.issn.1005-202X.2016.07.011]
[5]张文莉,吕晓琪,谷宇,等. 基于肺部CT图像中肺实质分割的研究进展[J].中国医学物理学杂志,2017,34(9):902.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.009]
　[J].Chinese Journal of Medical Physics,2017,34(11):902.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.009]
[6]刘欢,虞刚,路玉昆,等.带有预处理的计划CT和在线CT的形变配准方法[J].中国医学物理学杂志,2018,35(4):410.[doi:DOI:10.3969/j.issn.1005-202X.2018.04.008]
　LIU Huan,YU Gang,LU Yukun,et al.A preprocessing-based method for deformation registration of planned CT and online CT[J].Chinese Journal of Medical Physics,2018,35(11):410.[doi:DOI:10.3969/j.issn.1005-202X.2018.04.008]
[7]唐思源,刘燕茹,杨敏,等.基于CT图像的肺结节检测与识别[J].中国医学物理学杂志,2019,36(7):800.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.011]
　TANG Siyuan,LIU Yanru,YANG Min,et al.Detection and recognition of pulmonary nodules based on CT images[J].Chinese Journal of Medical Physics,2019,36(11):800.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.011]
[8]高磊,段辉宏,周韡鼎,等. 基于CT影像的肺叶分割技术研究进展[J].中国医学物理学杂志,2019,36(10):1168.[doi:DOI:10.3969/j.issn.1005-202X.2019.10.010]
　GAO Lei,DUAN Huihong,ZHOU Weiding,et al. Progress on CT image-based lung lobe segmentation techniques[J].Chinese Journal of Medical Physics,2019,36(11):1168.[doi:DOI:10.3969/j.issn.1005-202X.2019.10.010]
[9]曹宇,邢素霞,逄键梁,等.基于改进的VGG-16卷积神经网络的肺结节检测[J].中国医学物理学杂志,2020,37(7):940.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.026]
　CAO Yu,XING Suxia,PANG Jianliang,et al.Detection of pulmonary nodules based on improved VGG-16 convolution neural network[J].Chinese Journal of Medical Physics,2020,37(11):940.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.026]
[10]王乾梁,石宏理.基于改进YOLO V3的肺结节检测方法[J].中国医学物理学杂志,2021,38(9):1179.[doi:10.3969/j.issn.1005-202X.2021.09.024]
　WANG Qianliang,SHI Hongli,et al.Pulmonary nodule detection based on improved YOLO V3[J].Chinese Journal of Medical Physics,2021,38(11):1179.[doi:10.3969/j.issn.1005-202X.2021.09.024]

备注/Memo

备注/Memo:
【收稿日期】2019-07-15 【基金项目】国家自然科学基金青年科学基金（61702325） 【作者简介】张倩雯，硕士研究生，研究方向：图像处理、深度学习，E-mail: 718905052@qq.com 【通信作者】陈明，博士，教授，研究方向：数据仓库与数据挖掘、嵌入式系统、传感器技术等，E-mail: mchen@shou.edu.cn
更新日期/Last Update: 2019-11-28