Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning_Journal of Biomedical Engineering

Authors：

WANG Jingxuan ¹ ,  LIN Lan ¹ , ZHAO Siyuan ² , WU Xuetao ¹ , WU Shuicai ¹

1. College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China;
2. College of software engineering, Beijing University of Technology, Beijing 100124, P.R.China;

Corresponding author：

LIN Lan, Email: lanlin@bjut.edu.cn

Keywords：

deep learning; pulmonary nodules; detection; classification

DOI：

10.7507/1001-5515.201806019

Video：

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Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.

Citation： WANG Jingxuan, LIN Lan, ZHAO Siyuan, WU Xuetao, WU Shuicai. Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning. Journal of Biomedical Engineering, 2019, 36(4): 670-676. doi: 10.7507/1001-5515.201806019 Copy

1.	段纪俊, 严亚琼, 杨念念, 等. 中国恶性肿瘤发病与死亡的国际比较分析. 中国医学前沿杂志: 电子版, 2016, 8(7): 17-23.
2.	陈诺, 石毓君. 肺癌发病, 诊断及治疗相关研究进展. 世界最新医学信息文摘, 2018, 18(6): 114-116.
3.	Sverzellati N, Silva M, Calareso G, et al. Low-dose computed tomography for lung cancer screening: comparison of performance between annual and biennial screen. Eur Radiol, 2016, 26(11): 3821-3829.
4.	徐红卫. 低剂量CT在早期肺癌筛查中的应用及影像学表现. 深圳中西医结合杂志, 2017, 27(5): 75-76.
5.	郑光远, 刘峡壁, 韩光辉, 等. 医学影像计算机辅助检测与诊断系统综述. 软件学报, 2018, 29(5): 1471-1514.
6.	Han Fangfang, Wang Huafeng, Zhang Guopeng, et al. Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging, 2015, 28(1): 99-115.
7.	罗红兵, 周鹏, 青浩渺, 等. 计算机辅助检测系统在低剂量 CT 肺癌筛查中非钙化肺结节检出方法的研究. 肿瘤预防与治疗, 2017, 30(1): 33-38.
8.	Armato I S, Mclennan G, Bidaut L A, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 2011, 38(2): 915-931.
9.	Setio A A, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal, 2017, 42: 1-13.
10.	Dou Qi, Chen Hao, Yu Lequan, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
11.	田苗, 林岚, 张柏雯, 等. 深度学习在神经影像中的应用研究. 中国医疗设备, 2016, 31(12): 4-9.
12.	Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
13.	刘飞, 张俊然, 杨豪. 基于深度学习的医学图像识别研究进展. 中国生物医学工程学报, 2018, 37(1): 86-94.
14.	杨佳玲, 赵涓涓, 强彦, 等. 基于深度信念网络的肺结节良恶性分类. 科学技术与工程, 2016, 16(32): 69-74.
15.	Chen Sihong, Qin Jing, Ji Xing, et al. Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images. IEEE Trans Med Imaging, 2017, 36(3): 802-814.
16.	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks//25th International Conference on Neural Information Processing Systems. Lake Tahoe: Neural Information Processing Systems, 2012(1): 1097-1105.
17.	Han S S, Park G H, Lim W, et al. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PLoS One, 2018, 13(1): e0191493.
18.	Kang Guixia, Liu Kui, Hou Beibei, et al. 3D multi-view convolutional neural networks for lung nodule classification. PLoS One, 2017, 12(11): e0188290.
19.	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//2016 IEEE Conference On Computer Vision And Pattern Recognition (CPVR), Las Vegas: IEEE Computer Society, 2016: 770-778.
20.	Shin H C, Roth H R, Gao Mingchen, et al. Three aspects on using convolutional neural networks for computer-aided detection in medical imaging//Deep Learning and Convolutional Neural Networks for Medical Image Computing, Hawaii: IEEE Computer Society, 2017: 113-136.
21.	Depeursinge A, Vargas A, Platon A A, et al. Building a reference multimedia database for interstitial lung diseases. Computerized Medical Imaging and Graphics, 2012, 36(3): 227-238.
22.	Shi Zhenghao, Hao Huan, Zhao Minghua, et al. A deep CNN based transfer learning method for false positive reduction. Multimed Tools Appl, 2019, 78(1): 1017-1033.
23.	Nibali A, He Zhen, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg, 2017, 12(10): 1799-1808.
24.	Christiansen P, Nielsen L N, Steen K A, et al. DeepAnomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors, 2016, 16(11): 1904-1925.
25.	Wei Kaiqiang, Zhao Xu. Multiple-branches faster RCNN for human parts detection and pose estimation//Computer Vision-ACCV 2016 Workshops, 2017, 10118: 453-462.
26.	Qiu Xin, Yuan Chun. Improving object detection with convolutional neural network via iterative mechanism// Neural Information Processing: 24th International Conference, Guangzhou: Asia Pacific Neural Network Society, 2017: 141-150.
27.	Ding J, Li A, Hu Z, et al. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks//International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec: MICCAI Society, 2017: 559-567.
28.	Sun Wenqing, Zheng Bin, Qian Wei. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89: 530-539.
29.	陈思宏. 肺结节CT图像中基于多任务特征的语义属性自动评级. 深圳: 深圳大学, 2017.

1. 段纪俊, 严亚琼, 杨念念, 等. 中国恶性肿瘤发病与死亡的国际比较分析. 中国医学前沿杂志: 电子版, 2016, 8(7): 17-23.
2. 陈诺, 石毓君. 肺癌发病, 诊断及治疗相关研究进展. 世界最新医学信息文摘, 2018, 18(6): 114-116.
3. Sverzellati N, Silva M, Calareso G, et al. Low-dose computed tomography for lung cancer screening: comparison of performance between annual and biennial screen. Eur Radiol, 2016, 26(11): 3821-3829.
4. 徐红卫. 低剂量CT在早期肺癌筛查中的应用及影像学表现. 深圳中西医结合杂志, 2017, 27(5): 75-76.
5. 郑光远, 刘峡壁, 韩光辉, 等. 医学影像计算机辅助检测与诊断系统综述. 软件学报, 2018, 29(5): 1471-1514.
6. Han Fangfang, Wang Huafeng, Zhang Guopeng, et al. Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging, 2015, 28(1): 99-115.
7. 罗红兵, 周鹏, 青浩渺, 等. 计算机辅助检测系统在低剂量 CT 肺癌筛查中非钙化肺结节检出方法的研究. 肿瘤预防与治疗, 2017, 30(1): 33-38.
8. Armato I S, Mclennan G, Bidaut L A, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 2011, 38(2): 915-931.
9. Setio A A, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal, 2017, 42: 1-13.
10. Dou Qi, Chen Hao, Yu Lequan, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
11. 田苗, 林岚, 张柏雯, 等. 深度学习在神经影像中的应用研究. 中国医疗设备, 2016, 31(12): 4-9.
12. Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
13. 刘飞, 张俊然, 杨豪. 基于深度学习的医学图像识别研究进展. 中国生物医学工程学报, 2018, 37(1): 86-94.
14. 杨佳玲, 赵涓涓, 强彦, 等. 基于深度信念网络的肺结节良恶性分类. 科学技术与工程, 2016, 16(32): 69-74.
15. Chen Sihong, Qin Jing, Ji Xing, et al. Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images. IEEE Trans Med Imaging, 2017, 36(3): 802-814.
16. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks//25th International Conference on Neural Information Processing Systems. Lake Tahoe: Neural Information Processing Systems, 2012(1): 1097-1105.
17. Han S S, Park G H, Lim W, et al. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PLoS One, 2018, 13(1): e0191493.
18. Kang Guixia, Liu Kui, Hou Beibei, et al. 3D multi-view convolutional neural networks for lung nodule classification. PLoS One, 2017, 12(11): e0188290.
19. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//2016 IEEE Conference On Computer Vision And Pattern Recognition (CPVR), Las Vegas: IEEE Computer Society, 2016: 770-778.
20. Shin H C, Roth H R, Gao Mingchen, et al. Three aspects on using convolutional neural networks for computer-aided detection in medical imaging//Deep Learning and Convolutional Neural Networks for Medical Image Computing, Hawaii: IEEE Computer Society, 2017: 113-136.
21. Depeursinge A, Vargas A, Platon A A, et al. Building a reference multimedia database for interstitial lung diseases. Computerized Medical Imaging and Graphics, 2012, 36(3): 227-238.
22. Shi Zhenghao, Hao Huan, Zhao Minghua, et al. A deep CNN based transfer learning method for false positive reduction. Multimed Tools Appl, 2019, 78(1): 1017-1033.
23. Nibali A, He Zhen, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg, 2017, 12(10): 1799-1808.
24. Christiansen P, Nielsen L N, Steen K A, et al. DeepAnomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors, 2016, 16(11): 1904-1925.
25. Wei Kaiqiang, Zhao Xu. Multiple-branches faster RCNN for human parts detection and pose estimation//Computer Vision-ACCV 2016 Workshops, 2017, 10118: 453-462.
26. Qiu Xin, Yuan Chun. Improving object detection with convolutional neural network via iterative mechanism// Neural Information Processing: 24th International Conference, Guangzhou: Asia Pacific Neural Network Society, 2017: 141-150.
27. Ding J, Li A, Hu Z, et al. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks//International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec: MICCAI Society, 2017: 559-567.
28. Sun Wenqing, Zheng Bin, Qian Wei. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89: 530-539.
29. 陈思宏. 肺结节CT图像中基于多任务特征的语义属性自动评级. 深圳: 深圳大学, 2017.

Journal of Biomedical Engineering

Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning

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