计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 54-59.doi: 10.11896/jsjkx.210400211

• 智慧医疗 • 上一篇    下一篇

基于改进CNN的低剂量CT图像肺结节自动检测

岳晴1, 尹健宇2, 王生生2   

  1. 1 吉林师范大学计算机学院 吉林 四平 136000
    2 吉林大学计算机科学与技术学院 长春 130000
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 王生生(wss@jlu.edu.cn)
  • 作者简介:(yueqing1994@qq.com)

Automatic Detection of Pulmonary Nodules in Low-dose CT Images Based on Improved CNN

YUE Qing1, YIN Jian-yu2, WANG Sheng-sheng2   

  1. 1 School of Computer Science,Jilin Normal University,Siping,Jilin136000,China
    2 College of Computer Science and Technology,Jilin University,Changchun 130000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:YUE Qing,born in 1994,master.Her main research interests include medical image analysis and machine learning.
    WANG Sheng-sheng,born in 1974,Ph.D,professor.His main research interests include the areas of computer vision,deep learning and data mining.

摘要: 随着空气污染日益严重,肺癌已成为发病率和死亡率增长速度最快的恶性肿瘤之一,严重危害人们的生命和健康。肺癌早期主要表现为肺结节的形式,如果在肺癌早期能够及时发现并治疗,将能够提高肺癌的治疗效果。低剂量螺旋CT具有采集速度快、成本低、辐射低的特点,因此被大量应用于对肺结节的诊断。目前,CT图像的诊断多采用传统的人工诊断方式与CAD系统诊断的方式,但这两种方式存在精确性低、泛化性差的缺点。针对上述问题,文中以医学辅助诊断领域中的肺结节检测问题为研究对象,提出了一种基于改进CNN的低剂量CT图像的肺结节自动检测算法。首先,对CT图像进行预处理,提取肺实质;其次,对cascade-rcnn候选结节筛选网络进行改进,以提取更高质量的目标;然后,提出了改进3D CNN的假阳性减少网络,提高了结节分类的准确性;最后,在LUNA16数据集上进行了实验,结果表明,与现有算法相比,所提算法在检测准确率上有所提升。

关键词: 3D CNN, cascade-rcnn, LUNA16, 肺结节检测, 假阳性降低

Abstract: With air pollution getting worse and worse,lung cancer has become one of the malignant tumors with the fastest increasing morbidity and mortality rate,which seriously endangers people's life and health.The early stage of lung cancer is mainly in the form of pulmonary nodules.If the early stage of lung cancer can be detected and treated in time,the treatment effect of lung cancer will be improved.Low-dose spiral CT is widely used in the diagnosis of pulmonary nodules because of its characteristics of fast acquisition speed,low cost and low radiation.At present,CT image diagnosis mostly adopts the traditional manual diagnosis and CAD system diagnosis,but these two methods have the disadvantages of low accuracy and poor generalization.In view of the above problems,this paper takes the detection of pulmonary nodules in the field of medical assisted diagnosis as the research object,and proposes an improved low-dose CT image automatic detection algorithm for pulmonary nodules based on CNN.Firstly,the CT images are preprocessed to extract the lung parenchyma.Secondly,the cascade-rcnn candidate nodule screening network is improved to extract higher quality targets.Thirdly,an improved 3D CNN false positive reduction network is proposed to improve the accuracy of nodular classification.Finally,experiments are carried out on Luna16 dataset.Compared with existing algorithms,the detection accuracy of the proposed algorithm is improved.

Key words: 3D CNN, Cascade-RCNN, False positive reduction, LUNA16, Pulmonary nodule detection

中图分类号: 

  • TP391
[1] SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2015[J].CA:A Cancer Journal for Clinicians,2015,65(1):5-29.
[2] ZHU W,VANG Y S,HUANG Y,et al.Deepem:Deep 3d convnets with em forweakly supervised pulmonary nodule detection[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2018:812-820.
[3] QIN Y,ZHENG H,ZHU Y M,et al.Simultaneous accurate detection of pulmonary nodules and false positive reduction using 3D CNNs[C]//2018 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2018:1005-1009.
[4] ZUO W,ZHOU F,LI Z,et al.Multi-resolution CNN and know-ledge transfer for candidate classification in lung nodule detection[J].IEEE Access,2019,7:32510-32521.
[5] DING J,LI A,HU Z,et al.Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2017:559-567.
[6] NASRULLAH N,SANG J,ALAM M S,et al.Automated lung nodule detection and classification using deep learning combined with multiple strategies[J].Sensors,2019,19(17):3722.
[7] JAME A.Reduced lung-cancer mortality with low-dose computed tomo graphic screening[J].The New England Journal of Medicine,2011,65(5):395-409.
[8] ZHANG Z,LI X,YOU Q,et al.Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection[J].International Journal of Imaging Systems and Technology,2019,29(1):42-49.
[9] JACOBS C,RIKXOORT E M,TWELLMANN T,et al.Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images[J].Medical Image Analysis,2014,18(2):374-384.
[10] ZUO Z,WANG G,SHUAI B,et al.Exemplar based deep discriminative and shareable feature learning for scene image classification[J].Pattern Recognition,2015,48(10):3004-3015.
[11] LONG F N,ZHU X S,GAN J Z.Ultrasound image segmentation of brachial plexus via convolutional neural networks[J].Journal of Hefei University of Technology(Natural Science),2018,41(9):1191-1195,1296.
[12] ZHU W,LIU C,FAN W,et al.Deeplung:Deep 3d dual pathnets for automated pulmonary nodule detection and classification[C]//2018 IEEE Winter Conference on Applications of Computer Vision(WACV).IEEE,2018:673-681.
[13] JIN H,LI Z,TONG R,et al.A deep 3D residual CNN for false positive reduction in pulmonary nodule detection[J].Medical Physics,2018,45(5):2097-2107.
[14] CAI Z,VASCONCELOS N.Cascade r-cnn:Delving into highquality object detection[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2018:6154-6162.
[15] JI S,XU W,YANG M,et al.3D convolutional neural networks for human action recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,35(1):221-231.
[16] SETIO A A,JACOBS C,GELDERBLOM J,et al.Automatic detection of large pulmonary solid nodules in thoracic CT images[J].Medical physics,2015,42(10):5642-5653.
[17] KRISHNAMURTHY S,NARASIMHAN G,RENGASAMYU.An automatic computerized model for cancerous lung nodule detection from computed tomography images with reduced false positives[C]//International Conference on Recent Trends in Image Processing and Pattern Recognition.Springer,Singapore,2016:343-355.
[18] GOLAN R,JACOB C,DENZINGER J.Lung nodule detection in CT images using deep convolutional neural networks[C]//2016 International Joint Conference on Neural Networks(IJCNN).IEEE.2016:243-250.
[19] SHEN W,ZHOU M,YANG F,et al.Multi-scale convolutional neural networks for lung nodule classification[C]//International Conference on Information Processing in Medical Imaging.Springer.2015:588-599.
[20] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[21] GAO H,ZHU X,LIN S,et al.Deformable kernels:Adapting effective receptive fields for object deformation[J].arXiv:1910.02940,2019.
[22] XU S,LU H,YE M,et al.Improved Cascade R-CNN for Medical Images of Pulmonary Nodules Detection Combining Dilated HRNet[C]//Proceedings of the 2020 12th International Confe-rence on Machine Learning and Computing.2020:283-288.
[23] ZHAO L,WANG J,LI X,et al.Deep convolutional neural networks with merge-and-run mappings[J].arXiv:1611.07718,2016.
[24] GAO S,CHENG M M,ZHAO K,et al.Res2net:A new multi-scale backbone architecture[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,43(2):652-662.
[25] GRUETZEMACHER R,GUPTA A.Using Deep Learning for Pulmonary Nodule Detection & Diagnosis[C]//Proceedings of the Twenty-Second Americas Conference on Information Systems,San Diego,CA,USA,2016.
[26] GU J,TIAN Z,QI Y.Pulmonary nodules detection based on deformable convolution[J].IEEE Access,2020,8:16302-16309.
[27] DOU Q,CHEN H,YU L,et al.Multi-level contextual 3D CNNs for false positive reduction in pulmonary nodule detection[J].IEEE Transactions on Biomedical Engineering,2017,64:1558-1567.
[28] PENG H,SUN H,GUO Y.3D multi-scale deep convolutionalneural networks for pulmonary nodule detection[J].Plos one,2021,16(1):e0244406.
[1] 陈志强, 韩萌, 李慕航, 武红鑫, 张喜龙.
数据流概念漂移处理方法研究综述
Survey of Concept Drift Handling Methods in Data Streams
计算机科学, 2022, 49(9): 14-32. https://doi.org/10.11896/jsjkx.210700112
[2] 王明, 武文芳, 王大玲, 冯时, 张一飞.
生成链接树:一种高数据真实性的反事实解释生成方法
Generative Link Tree:A Counterfactual Explanation Generation Approach with High Data Fidelity
计算机科学, 2022, 49(9): 33-40. https://doi.org/10.11896/jsjkx.220300158
[3] 张佳, 董守斌.
基于评论方面级用户偏好迁移的跨领域推荐算法
Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer
计算机科学, 2022, 49(9): 41-47. https://doi.org/10.11896/jsjkx.220200131
[4] 周芳泉, 成卫青.
基于全局增强图神经网络的序列推荐
Sequence Recommendation Based on Global Enhanced Graph Neural Network
计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085
[5] 宋杰, 梁美玉, 薛哲, 杜军平, 寇菲菲.
基于无监督集群级的科技论文异质图节点表示学习方法
Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level
计算机科学, 2022, 49(9): 64-69. https://doi.org/10.11896/jsjkx.220500196
[6] 柴慧敏, 张勇, 方敏.
基于特征相似度聚类的空中目标分群方法
Aerial Target Grouping Method Based on Feature Similarity Clustering
计算机科学, 2022, 49(9): 70-75. https://doi.org/10.11896/jsjkx.210800203
[7] 郑文萍, 刘美麟, 杨贵.
一种基于节点稳定性和邻域相似性的社区发现算法
Community Detection Algorithm Based on Node Stability and Neighbor Similarity
计算机科学, 2022, 49(9): 83-91. https://doi.org/10.11896/jsjkx.220400146
[8] 吕晓锋, 赵书良, 高恒达, 武永亮, 张宝奇.
基于异质信息网的短文本特征扩充方法
Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network
计算机科学, 2022, 49(9): 92-100. https://doi.org/10.11896/jsjkx.210700241
[9] 徐天慧, 郭强, 张彩明.
基于全变分比分隔距离的时序数据异常检测
Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance
计算机科学, 2022, 49(9): 101-110. https://doi.org/10.11896/jsjkx.210600174
[10] 聂秀山, 潘嘉男, 谭智方, 刘新放, 郭杰, 尹义龙.
基于自然语言的视频片段定位综述
Overview of Natural Language Video Localization
计算机科学, 2022, 49(9): 111-122. https://doi.org/10.11896/jsjkx.220500130
[11] 曹晓雯, 梁美玉, 鲁康康.
基于细粒度语义推理的跨媒体双路对抗哈希学习模型
Fine-grained Semantic Reasoning Based Cross-media Dual-way Adversarial Hashing Learning Model
计算机科学, 2022, 49(9): 123-131. https://doi.org/10.11896/jsjkx.220600011
[12] 周旭, 钱胜胜, 李章明, 方全, 徐常胜.
基于对偶变分多模态注意力网络的不完备社会事件分类方法
Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification
计算机科学, 2022, 49(9): 132-138. https://doi.org/10.11896/jsjkx.220600022
[13] 戴禹, 许林峰.
基于文本行匹配的跨图文本阅读方法
Cross-image Text Reading Method Based on Text Line Matching
计算机科学, 2022, 49(9): 139-145. https://doi.org/10.11896/jsjkx.220600032
[14] 曲倩文, 车啸平, 曲晨鑫, 李瑾如.
基于信息感知的虚拟现实用户临场感研究
Study on Information Perception Based User Presence in Virtual Reality
计算机科学, 2022, 49(9): 146-154. https://doi.org/10.11896/jsjkx.220500200
[15] 周乐员, 张剑华, 袁甜甜, 陈胜勇.
多层注意力机制融合的序列到序列中国连续手语识别和翻译
Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion
计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!