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.	段紀俊, 嚴亞瓊, 楊念念, 等. 中國惡性腫瘤發病與死亡的國際比較分析. 中國醫學前沿雜志: 電子版, 2016, 8(7): 17-23.
2.	陳諾, 石毓君. 肺癌發病, 診斷及治療相關研究進展. 世界最新醫學信息文摘, 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.	徐紅衛. 低劑量CT在早期肺癌篩查中的應用及影像學表現. 深圳中西醫結合雜志, 2017, 27(5): 75-76.
5.	鄭光遠, 劉峽壁, 韓光輝, 等. 醫學影像計算機輔助檢測與診斷系統綜述. 軟件學報, 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.	羅紅兵, 周鵬, 青浩渺, 等. 計算機輔助檢測系統在低劑量 CT 肺癌篩查中非鈣化肺結節檢出方法的研究. 腫瘤預防與治療, 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, 林嵐, 張柏雯, 等. 深度學習在神經影像中的應用研究. 中國醫療設備, 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.	劉飛, 張俊然, 楊豪. 基于深度學習的醫學圖像識別研究進展. 中國生物醫學工程學報, 2018, 37(1): 86-94.
14.	楊佳玲, 趙涓涓, 強彥, 等. 基于深度信念網絡的肺結節良惡性分類. 科學技術與工程, 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.	陳思宏. 肺結節CT圖像中基于多任務特征的語義屬性自動評級. 深圳: 深圳大學, 2017.

1. 段紀俊, 嚴亞瓊, 楊念念, 等. 中國惡性腫瘤發病與死亡的國際比較分析. 中國醫學前沿雜志: 電子版, 2016, 8(7): 17-23.
2. 陳諾, 石毓君. 肺癌發病, 診斷及治療相關研究進展. 世界最新醫學信息文摘, 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. 徐紅衛. 低劑量CT在早期肺癌篩查中的應用及影像學表現. 深圳中西醫結合雜志, 2017, 27(5): 75-76.
5. 鄭光遠, 劉峽壁, 韓光輝, 等. 醫學影像計算機輔助檢測與診斷系統綜述. 軟件學報, 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. 羅紅兵, 周鵬, 青浩渺, 等. 計算機輔助檢測系統在低劑量 CT 肺癌篩查中非鈣化肺結節檢出方法的研究. 腫瘤預防與治療, 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, 林嵐, 張柏雯, 等. 深度學習在神經影像中的應用研究. 中國醫療設備, 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. 劉飛, 張俊然, 楊豪. 基于深度學習的醫學圖像識別研究進展. 中國生物醫學工程學報, 2018, 37(1): 86-94.
14. 楊佳玲, 趙涓涓, 強彥, 等. 基于深度信念網絡的肺結節良惡性分類. 科學技術與工程, 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. 陳思宏. 肺結節CT圖像中基于多任務特征的語義屬性自動評級. 深圳: 深圳大學, 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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