利用深度学习技术辅助肺结节的人工智能检测
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摘要
<正>在中国,肺癌是发病率最高和死亡率最高的恶性肿瘤,2015年新增癌症患者429万,其中肺癌新发病例达到73.3万,死亡人数更是高达61万~([1])。我国约75%的肺癌患者在确诊时候就已经属于晚期,5年生存率不到20%,这与缺乏筛查以及科学有效鉴别肺结节有关。肺结节影像学表现为直径≤3 cm的局灶性、类圆形、密度增高的实性或亚实性
引文
I Chen W, Zheng R, Baade PD, et al. Cancer statistics in China,2015. CA Cancer J Clin, 2016, 66(2):115-132.
2中华医学会呼吸病学分会肺癌学组,中国肺癌防治联盟专家组.肺结节诊治中国专家共识(2018年版).中华结核和呼吸杂志,2018, 41(10):763-771.
3 National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5):395-409.
4 Rubin GD, Roos JE, Tall M, et al. Characterizing search,recognition, and decision in the detection of lung nodules on CT scans:elucidation with eye tracking. Radiology, 2015, 274(1):276-286.
5 Armato SG, Roberts RY, Kocherginsky M, et al. Assessment of radiologist performance in the detection of lung nodules:dependence on the definition of"truth". Acad Radiol, 2009, 16(1):28-38.
6崔云,马大庆.肺结节的CT计算机辅助检测和诊断的基本方法和应用.中国医学影像术,2007, 23(3):469-472.
7 Jacobs C, van Rikxoort EM, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal, 2014, 18(2):374-384.
8 Cheng JZ, Ni D, Chou YH, et al. Computer-aided diagnosis with deep learning architecture:applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6:24454.
9 Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning:a primer for radiologists. Radio graphics, 2017, 37(7):2113-2131.
10 LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4):541-551.
11 Rosenblatt F. The perceptron:a probabilistic model for information storage and organization in the brain. Psychol Rev,1958,65(6):386-408.
12 Fukushima K. Neocognitron:a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4):193-202.
13 Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11):2278-2324.
14 Chopra S, Hadsell R, LeCun Y. Learning a similarity metric discriminatively, with application to face verification. IEEE Computer Society Conference, 2015, 1:539-546.
15 Dan CC, Ueli M, Luca MG, et al. Deep, big, simple neural nets for handwritten digit recognition. Neural Comput, 2010, 22(12):3207-3220.
16 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60(2):84-90.
17 Sermanet P, Eigen D, Zhang X, et al. OverFeat:integrated recognition, localization and detection using convolutional networks. Proc ICLR, 2015, arXiv:1312.6229v4. https://arxiv.org/pdf/1312.6229.pdf.
18 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proc ICLR,2015.arXiv:1409.1556v6. https://arxiv.org/pdf/1409.1556v6.pdf.
19 Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions.2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Boston, MA, 2015:1-9. https://ieeexplore.ieee.org/document/7298594. doi:10.1109/CVPR.2015.7298594.
20 He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, NV, 2016:770-778.https://ieeexplore.ieee.org/document/7780459. doi:10.1109/CVPR.2016.90.
21 Huang G, Liu Z, Laurens VDM, et al. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, 2017:2261-2269. https://ieeexplore.ieee.org/document/8099726. doi:10.1109/CVPR.2017.243.
22 Girshick R, Donahue J, Darrell T, et al. Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell, 2016, 38(1):142-158.
23 He KM, Zhang XY, Ren SQ, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2014, 37(9):1904-1916.
24 Girshick R. Fast R-CNN. 2015 IEEE International Conference on Computer Vision(ICCV), Santiago, 2015:1440-1448. doi:10.1109/ICCV.2015.169.
25 Ruhan S, Owens W, Wiegand R, et al. Intervertebral disc detection in X-ray images using faster R-CNN. Conf Proc IEEE Eng Med Biol Soc, 2017:564-567.
26 Dai JF, Li Y, He KM, et al. R-FCN:object detection via regionbased fully convolutional networks. 2016. https://arxiv.org/pdf/1605.06409.pdf.
27 Dai JF, Qi HZ, Xiong YW, et al. Deformable convolutional networks. 2017 IEEE International Conference on Computer Vision(ICCV), Venice, italy, 2017:764-773. doi:10.1109/ICCV.
28 Liu ST, Huang D, Wang YH. Receptive field block net for accurate and fast object detection. 2017. doi:10.1007/978-3-030-01252-6_24.
29 Al-Masni MA, Al-Antari MA, Park JM, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.Comput Meth Prog Bio, 2018, 157:85-94.
30 Liu W, Anguelov D, Erhan D, et al. SSD:single shot multibox detector. European Conference on Computer Vision(ECCV)2016:21-37. https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_2. doi:10.1007/978-3-319-46448-0_2.
31 Litjens G, Sanchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016, 6:26286.
32 Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell,2017, 39(4):640-651.
33 Somasundaram SK, Alli P. A machine learning ensemble classifierfor early prediction of diabetic retinopathy. J Med Syst, 2017,41(12):201.
34 Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature, 1986, 323(6088):533-536.
35 Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7):1527-1554.
36 Bengio Y, Lamblin P, Dan P, et al. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst, 2007,19:153-160.
37 Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders. Proceedings of the25th International Conference on Machine Learning Pages. ACM,2008:1096-1103. https://dl.acm.org/citation.cfm?doid=1390156.1390294. doi:10.1145/1390156.1390294.
38 Hinton G. A practical guide to training restricted Boltzmann machines. Momentum, 2010, 9(1):926.
39 Ginneken VB. Fifty years of computer analysis in chest imaging:rule-based, machine learning, deep learning. Radiol Phys Technol,2017,10(1):23-32.
40 Li W, Cao P, Zhao DZ, et al. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Method Med, 2016:6215085.
41 Gruetzemacher R, Gupta A. Using deep learning for pulmonary nodule detection&diagnosis. Twenty-second Americas conference on information systems, San Diego 2016. https://aisel.aisnet.org/amcis2016/Intel/Presentations/3/.
42 Hussein S, Gillies R, Cao K, et al. TumorNet:lung nodule characterization using multi-view convolutional neural network with gaussian process. 2017 IEEE 14th International Symposium on Biomedical Imaging(ISBI 2017), Melbourne, VIC, 2017:1007-1010. https://ieeexplore.ieee.org/document/7950686. doi:10.1109/ISBI:2017.7950686.
43 Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J Comput Ass Rad, 2017, 12(10):1799-1808.
44 Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images.J Healthc Eng, 2017:8314740.
45侍新,谢世朋,李海波.基于卷积神经网络检测肺结节.中国医学影像技术,2018, 34(6):934-939.
46吕晓琪,吴凉,谷宇,等.基于三维卷积神经网络的低剂量CT肺结节检测.光学精密工程,2018,26(5):213-220.
47巩萍,王姗姗,罗举建.基于稀疏自编码神经网络的肺结节特征提取及良恶性分类.医疗卫生装备,2015, 36(12):7-10.
48赵鑫,强彦,强梓林,等.基于局部感受野和半监督深度自编码的肺结节检测方法.科学技术与工程,2017, 17(33):125-130.
49张婷,赵涓涓,罗嘉滢,等.基于多视角深度信念网络的肺结节识别方法.科学技术与工程,2018,18(5):92-98.
50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.
51 Sun WQ, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89:530-539.
52 Hua KL, Hsu CH, Hidayati SC, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther, 2015, 8:2015-2022.
2中华医学会呼吸病学分会肺癌学组,中国肺癌防治联盟专家组.肺结节诊治中国专家共识(2018年版).中华结核和呼吸杂志,2018, 41(10):763-771.
3 National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5):395-409.
4 Rubin GD, Roos JE, Tall M, et al. Characterizing search,recognition, and decision in the detection of lung nodules on CT scans:elucidation with eye tracking. Radiology, 2015, 274(1):276-286.
5 Armato SG, Roberts RY, Kocherginsky M, et al. Assessment of radiologist performance in the detection of lung nodules:dependence on the definition of"truth". Acad Radiol, 2009, 16(1):28-38.
6崔云,马大庆.肺结节的CT计算机辅助检测和诊断的基本方法和应用.中国医学影像术,2007, 23(3):469-472.
7 Jacobs C, van Rikxoort EM, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal, 2014, 18(2):374-384.
8 Cheng JZ, Ni D, Chou YH, et al. Computer-aided diagnosis with deep learning architecture:applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6:24454.
9 Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning:a primer for radiologists. Radio graphics, 2017, 37(7):2113-2131.
10 LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4):541-551.
11 Rosenblatt F. The perceptron:a probabilistic model for information storage and organization in the brain. Psychol Rev,1958,65(6):386-408.
12 Fukushima K. Neocognitron:a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4):193-202.
13 Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11):2278-2324.
14 Chopra S, Hadsell R, LeCun Y. Learning a similarity metric discriminatively, with application to face verification. IEEE Computer Society Conference, 2015, 1:539-546.
15 Dan CC, Ueli M, Luca MG, et al. Deep, big, simple neural nets for handwritten digit recognition. Neural Comput, 2010, 22(12):3207-3220.
16 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60(2):84-90.
17 Sermanet P, Eigen D, Zhang X, et al. OverFeat:integrated recognition, localization and detection using convolutional networks. Proc ICLR, 2015, arXiv:1312.6229v4. https://arxiv.org/pdf/1312.6229.pdf.
18 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proc ICLR,2015.arXiv:1409.1556v6. https://arxiv.org/pdf/1409.1556v6.pdf.
19 Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions.2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Boston, MA, 2015:1-9. https://ieeexplore.ieee.org/document/7298594. doi:10.1109/CVPR.2015.7298594.
20 He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, NV, 2016:770-778.https://ieeexplore.ieee.org/document/7780459. doi:10.1109/CVPR.2016.90.
21 Huang G, Liu Z, Laurens VDM, et al. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, 2017:2261-2269. https://ieeexplore.ieee.org/document/8099726. doi:10.1109/CVPR.2017.243.
22 Girshick R, Donahue J, Darrell T, et al. Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell, 2016, 38(1):142-158.
23 He KM, Zhang XY, Ren SQ, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2014, 37(9):1904-1916.
24 Girshick R. Fast R-CNN. 2015 IEEE International Conference on Computer Vision(ICCV), Santiago, 2015:1440-1448. doi:10.1109/ICCV.2015.169.
25 Ruhan S, Owens W, Wiegand R, et al. Intervertebral disc detection in X-ray images using faster R-CNN. Conf Proc IEEE Eng Med Biol Soc, 2017:564-567.
26 Dai JF, Li Y, He KM, et al. R-FCN:object detection via regionbased fully convolutional networks. 2016. https://arxiv.org/pdf/1605.06409.pdf.
27 Dai JF, Qi HZ, Xiong YW, et al. Deformable convolutional networks. 2017 IEEE International Conference on Computer Vision(ICCV), Venice, italy, 2017:764-773. doi:10.1109/ICCV.
28 Liu ST, Huang D, Wang YH. Receptive field block net for accurate and fast object detection. 2017. doi:10.1007/978-3-030-01252-6_24.
29 Al-Masni MA, Al-Antari MA, Park JM, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.Comput Meth Prog Bio, 2018, 157:85-94.
30 Liu W, Anguelov D, Erhan D, et al. SSD:single shot multibox detector. European Conference on Computer Vision(ECCV)2016:21-37. https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_2. doi:10.1007/978-3-319-46448-0_2.
31 Litjens G, Sanchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016, 6:26286.
32 Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell,2017, 39(4):640-651.
33 Somasundaram SK, Alli P. A machine learning ensemble classifierfor early prediction of diabetic retinopathy. J Med Syst, 2017,41(12):201.
34 Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature, 1986, 323(6088):533-536.
35 Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7):1527-1554.
36 Bengio Y, Lamblin P, Dan P, et al. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst, 2007,19:153-160.
37 Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders. Proceedings of the25th International Conference on Machine Learning Pages. ACM,2008:1096-1103. https://dl.acm.org/citation.cfm?doid=1390156.1390294. doi:10.1145/1390156.1390294.
38 Hinton G. A practical guide to training restricted Boltzmann machines. Momentum, 2010, 9(1):926.
39 Ginneken VB. Fifty years of computer analysis in chest imaging:rule-based, machine learning, deep learning. Radiol Phys Technol,2017,10(1):23-32.
40 Li W, Cao P, Zhao DZ, et al. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Method Med, 2016:6215085.
41 Gruetzemacher R, Gupta A. Using deep learning for pulmonary nodule detection&diagnosis. Twenty-second Americas conference on information systems, San Diego 2016. https://aisel.aisnet.org/amcis2016/Intel/Presentations/3/.
42 Hussein S, Gillies R, Cao K, et al. TumorNet:lung nodule characterization using multi-view convolutional neural network with gaussian process. 2017 IEEE 14th International Symposium on Biomedical Imaging(ISBI 2017), Melbourne, VIC, 2017:1007-1010. https://ieeexplore.ieee.org/document/7950686. doi:10.1109/ISBI:2017.7950686.
43 Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J Comput Ass Rad, 2017, 12(10):1799-1808.
44 Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images.J Healthc Eng, 2017:8314740.
45侍新,谢世朋,李海波.基于卷积神经网络检测肺结节.中国医学影像技术,2018, 34(6):934-939.
46吕晓琪,吴凉,谷宇,等.基于三维卷积神经网络的低剂量CT肺结节检测.光学精密工程,2018,26(5):213-220.
47巩萍,王姗姗,罗举建.基于稀疏自编码神经网络的肺结节特征提取及良恶性分类.医疗卫生装备,2015, 36(12):7-10.
48赵鑫,强彦,强梓林,等.基于局部感受野和半监督深度自编码的肺结节检测方法.科学技术与工程,2017, 17(33):125-130.
49张婷,赵涓涓,罗嘉滢,等.基于多视角深度信念网络的肺结节识别方法.科学技术与工程,2018,18(5):92-98.
50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.
51 Sun WQ, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89:530-539.
52 Hua KL, Hsu CH, Hidayati SC, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther, 2015, 8:2015-2022.
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