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收稿：2021-06-22，
修回：2021-12-4，
录用：2021-12-11，
纸质出版：2022-03-16
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目的
2
肺结节检测在低剂量肺部计算机断层扫描（computed tomography，CT）筛查肺癌中具有重要意义。但由于结节大小、形状和密度的变化十分复杂，导致难以在低假阳性率下保证高的灵敏度，这限制了深度学习算法在常规临床实践中的肺结节自动诊断，建立具有良好结节检测性能的深度学习模型仍然是一个挑战。针对此问题，本文提出了一种基于3D ReSidual U（3D RSU）块的嵌套U结构的肺结节检测框架。
方法
2
3D RSU块通过混合不同大小的感受场获得多尺度特征来丰富特征信息。而嵌套U结构允许网络获得更大分辨率的特征图，从而具有多层次深度特征，获得丰富的局部和全局信息，增强网络区分前景和背景的能力，进而提高微小结节等非显著性目标的检测性能。
结果
2
该框架在公共肺结节（lung nodule analysis 16）挑战数据集上进行了评价。方法能够准确地检测出肺结节，灵敏度达到了97.2%，与基准方法相比，该方法灵敏度提高了2.6%，具有很高的灵敏度和特异性，在0.125、0.25、0.5、1、2、4、8共7个假阳率点的灵敏度平均值为86.4%，尤其是在每扫描0.25个假阳性上，灵敏度达到80.9%，优于基准算法76.9%的结果。
结论
2
本文所提出的结节检测模型，由于在低假阳率上有较高的灵敏度，可以使本网络在常规临床实践中为医生辅助诊断提供更加可靠清晰的早期肺癌参考信息。
Objective
2
Lung cancer is one of the most common diseases in humans and mainly causes the rising mortality rate. Medical experts believe that the early diagnosis of lung cancer can reduce mortality by screening for lung nodules through computed tomography(CT). Checking a large number of CT images can reduce lung cancer risk. However
CT scan images contain a large volume of information about nodules
and as the number of images increases
accuracy becomes a very challenging task for radiologists. Therefore
an effective computer-aided diagnosis (CAD) system should be designed. However
the high false-positive rate remains a challenging problem. In response
this paper proposes a new lung nodule detection framework based on 3D ReSidualU-blocks (3D RSU) module and nested U structure.
Method
2
This paper trains an end-to-end lung nodule detection model for one stage. The whole system can shorten the processing time of pulmonary nodule detection without reducing the accuracy of early detection. The 3D deep convolutional neural network (CNN) proposed in this paper is based on the region proposal network (RPN) of the Faster R-CNN Network. The 3D CNN can make full use of the spatial information of CT images. The problem of missed detection by the nodule and the large number of false-positive nodules during detection of nodules with small diameter nodules can be solved by dividing the front background information and enhancing the ability to detect non-salient objects in the image. In this paper
the 3D RSU was designed to capture multi-scale features within the stage. The symmetrical codec structure can be used to learn how to extract and encode multi-scale context information. By using the different layers of 3D RSU
the network can allow feature maps of any spatial resolution as input elements to extract elements at multiple scales. This process reduces the loss of detail caused by large-scale direct up-sampling. The 3D RSU was embedded into the network to form a nested u-shaped structure. This structure allows the network to obtain larger resolution feature maps
thus providing multiple levels of deep features. High-level semantic features can be transitioned to supplement low-level conventional features. Improve the detection performance of non-important targets such as calcification and ground glass. If high-resolution feature maps only are used for prediction
a large amount of lung nodule position information contained in low-resolution feature maps will be lost. Therefore
a feature pyramid network was designed in the decoding structure to integrate low-level high-resolution features. In addition to the strong high-level semantic features
the location details and strong semantics of nodule detection are enriched
and the detection of small nodules is realized.
Result
2
The framework was evaluated on the public lung nodule analysis (LUNA16) challenge data set containing 888 patient lung nodule labels. A total of 10 folds were observed in the entire data set. This article used a 10-fold cross-validation method to compare performance indicators. Our network training uses a stochastic gradient descent optimizer. The initial learning rate is 0.01. After reaching 1/3 and 2/3 of the total number of iterations
the learning rate was adjusted to 0.001 and 0.000 1
respectively
and the weight attenuation is 1×10
-4
. The batch size is 8
the number of model iterations is 120
one round of training takes approximately 620 s
and the training of a model takes approximately 20 h. Through ablation and comparative experiments
this paper discusses the advantages of 3D RSU module in pulmonary nodule detection performance compared with Res18
and the structural advantages of nested U structure over traditional U structure. Fewer false-positives means more correct nodules are identified with fewer errors
and more immediate help for doctors. Compared with the 3D ResNet-18 module
the 3D RSU module proposed in this paper has a significant improvement in low false positives. It shows that the 3D RSU module reduces the detail loss caused by large-scale direct-up sampling through codec structure and cascade operation. The semantic features of pulmonary nodules were used to obtain the highest number of low-level features of pulmonary nodules
to allow the network to directly extract multiple proportion features from each residual block. Therefore
compared with 3D Resnet-18 block
3D RSU block can obtain feature map with richer semantics and clearer location information. Notably
a low false-positive rate is important0 for the system to identify acceptable modules with less false-positive results. Therefore
the 3D RSU module has a more extensive clinical application value. In comparison with the traditional U-NET structure
the nested U structure proposed in this paper has a slightly higher parameter value than the Res18_FPN_RPN network
and the gap in the test time is almost negligible
indicating that although the structure of this article is more complex
the nested U structure does not remarkably. Increase the computational overhead. In comparison with benchmark experiments
our method can accurately detect lung nodules with a sensitivity of 97.1%
and the sensitivity increased by 2.5%. This method has high sensitivity and specificity. The average sensitivity of the seven false alarm rate points of 0.125
0.25
0.5
1
2 and 8 is 86.4%
especially when 0.25 false alarms are scanned per scan
in which the sensitivity reaches 80.9%
which is better than the result of 76.9%.
Conclusion
2
The nodule detection model proposed in this paper has high sensitivity in the low false-positive rate
thus allowing this network to provide more reliable and clear reference information for early lung cancer diagnosis in routine clinical practice for doctors.
Armato III S G, McLennan G, Bidaut L, McNitt-Gray M F, Meyer C R, Reeves A P, Zhao B S, Aberle D R, Henschke C I, Hoffman E A, Kazerooni E A, MacMahon H, Van Beek E J R, Yankelevitz D, Biancardi A M, Bland P H, Brown M S, Engelmann R M, Laderach G E, Max D, Pais R C, Qing D P Y, Roberts R Y, Smith A R, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish G W, Jude C M, Munden R F, Petkovska I, Quint L E, Schwartz L H, Sundaram B, Dodd L E, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele A V, Gupte S, Sallam M, Heath M D, Kuhn M H, Dharaiya E, Burns R, Fryd D S, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft B Y and Clarke L P. 2011. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical Physics, 2011, 38(2): 915-931[ DOI: 10.1118/1.3528204 http://dx.doi.org/10.1118/1.3528204 ]
Bray F, Ferlay J, Soerjomataram I, Siegel R L, Torre L A and Jemal A. 2018. Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA, USA: A Cancer Journal for Clinicians, 68(6): 394-424[DOI:10.3322/caac.21492]
Cao H C, Liu H, Song E M, Ma G Z, Xu X Y, Jin R C, Liu T Y and Hung C C. 2020. A two-stage convolutional neural networks for lung nodule detection. IEEE Journal of Biomedical and Health Informatics, 24(7): 2006-215[DOI:10.1109/JBHI.2019.2963720]
Chen D Z and Jiang Q. 2021. Pulmonary nodule detection based on convolutional neural networks with transfer learning. Computer Engineering and Design, 42(1): 240-247
陈道争, 江倩. 2021. 基于卷积神经网络和迁移学习的肺结节检测. 计算机工程与设计, 42(1): 240-247[DOI:10.16208/j.issn1000-7024.2021.01.035]
Dou Q, Chen H, Jin Y M, Lin H J, Qin J and Heng P A. 2017a. Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City, Canada: Springer: 630-638[ DOI: 10.1007/978-3-319-66179-7_72 http://dx.doi.org/10.1007/978-3-319-66179-7_72 ]
Dou Q, Chen H, Yu L Q, Qin J and Heng P A. 2017b. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Transactions on Biomedical Engineering, 64(7): 1558-1567[DOI:10.1109/TBME.2016.2613502]
Firmino M, Angelo G, Morais H, Dantas M R and Valentim R. 2016. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. BioMedical Engineering OnLine, 15(1): #2[DOI:10.1186/s12938-015-0120-7]
Gao H M, Zhao J J, Liu J H, Tang X X and Wang P. 2019. Multi-scale convolutional neural network for pulmonary nodule false positive reduction. Computer Engineering and Design, 40(9): 2718-2724
高慧明, 赵涓涓, 刘继华, 唐笑先, 王磐. 2019. 多尺度卷积神经网络用于肺结节假阳性降低. 计算机工程与设计, 40(9): 2718-2724[DOI:10.16208/j.issn1000-7024.2019.09.054]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 770-778[ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Hong M J, Wu G, Liu X C, Jia J C and Yang X H. 2021. Detection algorithm of lung nodule based on attention mechanism. Computer Engineering and Design, 42(1): 83-88
洪敏杰, 吴刚, 刘星辰, 贾俊铖, 杨歆豪. 2021. 基于注意力机制的肺结节检测算法. 计算机工程与设计, 42(1): 83-88[DOI:10.16208/j.issn1000-7024.2021.01.013]
Huang G, Liu Z, Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE: 2261-2269[ DOI: 10.1109/CVPR.2017.243 http://dx.doi.org/10.1109/CVPR.2017.243 ]
Huang K Q, Ren W Q and Tan T N. 2014. A review on image object classification and detection. Chinese Journal of Computers, 37(6): 1225-1240
黄凯奇, 任伟强, 谭铁牛. 2014. 图像物体分类与检测算法综述. 计算机学报, 37(6): 1225-1240[DOI:10.3724/SP.J.1016.2014.01225]
Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Advances in Neural Information Processing Systems. Lake Tahoe, USA: NIPS: 1106-1114
Li Y M and Fan Y. 2020. DeepSEED: 3D squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection//Proceedings of the 17th IEEE International Symposium on Biomedical Imaging (ISBI). Iowa City, USA: IEEE: 1866-1869[ DOI: 10.1109/ISBI45749.2020.9098317 http://dx.doi.org/10.1109/ISBI45749.2020.9098317 ]
Lin D T, Yan C R and Chen W T. 2005. Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system. Computerized Medical Imaging and Graphics, 29(6): 447-458[DOI:10.1016/j.compmedimag.2005.04.001]
Polat G, Dogrusoz Y S and Halici U. 2018. Effect of input size on the classification of lung nodules using convolutional neural networks//Proceedings of the 26th Signal Processing and Communications Applications Conference (SIU). Izmir, Turkey: SIU: 1-4
Qi Y J, Gu J H, Zhang Y J, Wang F and Tian Z P. 2020. Deep mixed convolution model for pulmonary nodule detection. Journal of Computer Applications, 40(10): 2904-2909
戚永军, 顾军华, 张亚娟, 王锋, 田泽培. 2020. 基于深度混合卷积模型的肺结节检测方法. 计算机应用, 40(10): 2904-2909[DOI:10.11772/j.issn.1001-9081.2020020192]
Qin X B, Zhang Z C, Huang C Y, Dehghan M, Zaiane O R and Jagersand M. 2020. U 2 -Net: going deeper with nested U-structure for salient object detection. Pattern Recognition,106: #107404[DOI:10.1016/j.patcog.2020.107404]
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z H, Karpathy A, Khosla A, Bernstein M, Berg A C and Li F F. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3): 211-252[DOI:10.1007/s11263-015-0816-y]
Setio A A A, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel S J, Wille M M W, Naqibullah M, Sánchez C I and Van Ginneken B. 2016. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging, 35(5): 1160-1169[DOI:10.1109/TMI.2016.2536809]
Setio A A A, Traverso A, De Bel T, Berens M S N, Van Den Bogaard C, Cerello P, Chen H, Dou Q, Fantacci M E, Geurts B, Van Der Gugten R, Heng P A, Jansen B, De Kaste M M J, Kotov V, Lin J Y H, Manders J T M C, Sóñora-Mengana A, García-Naranjo J C, Papavasileiou E, Prokop M, Saletta M, Schaefer-Prokop C M, Scholten E T, Scholten L, Snoeren M M, Torres E L, Vandemeulebroucke J, Walasek N, Zuidhof G C A, Van Ginneken B and Jacobs C. 2017. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 Challenge. Medical Image Analysis, 42: 1-13[DOI:10.1016/j.media.2017.06.015]
Shi L K, Ma H Q and Zhang J. 2021. Automatic detection of pulmonary nodules in CT images based on 3D Res-I network. The Visual Computer, 37(6): 1343-1356[DOI:10.1007/s00371-020-01869-7]
Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition//Proceedings of the 3rd International Conference on Learning Representations. San Diego, USA: ICLR
Sori W J, Feng J, Godana A W, Liu S H and Gelmecha D J. 2021. DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Frontiers of Computer Science, 15(2): #152701[DOI:10.1007/s11704-020-9050-z]
Sun L M, Wang Z R, Pu H, Yuan G H, Guo L, Pu T and Peng Z M. 2021. Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection. Computers in Biology and Medicine, 133:#104357[DOI:10.1016/j.compbiomed.2021.104357]
Wang H H. 2018. Deep Learning Based Pulmonary Nodule Detection Algorithms for Medical Images. Xi'an: Xidian University
王厚华. 2018. 基于深度学习的医学图像肺结节检测算法研究. 西安: 西安电子科技大学
Xie H T, Yang D B, Sun N N, Chen Z N and Zhang Y D. 2019. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognition, 85: 109-119[DOI:10.1016/j.patcog.2018.07.031]
Xie S N, Girshick R, Dollár P, Tu Z W and He K M. 2017. Aggregated residual transformations for deep neural networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE: 5987-5995[ DOI: 10.1109/CVPR.2017.634 http://dx.doi.org/10.1109/CVPR.2017.634 ]
Zhang F L and Zhang S M. 2020. Review of deep learning methods applied to lung nodule detection in CT images. Computer Engineering and Applications, 56(13): 20-32
张福玲, 张少敏. 2020. 应用于CT图像肺结节检测的深度学习方法综述. 计算机工程与应用, 56(13): 20-32[DOI:10.3778/j.issn.1002-8331.2002-0051]
Zhang G, Lin L and Wang J X. 2021. Lung nodule classification in CT images using 3D DenseNet. Journal of Physics: Conference Series, 1827: #012155[DOI:10.1088/1742-6596/1827/1/012155]
Zhou Z W, Siddiquee M R, Tajbakhsh N and Liang J M. 2018. UNet++: a nested U-Net architecture for medical image segmentation//4th Deep Learning in Medical Image Analysis. Granada: MICCAI: 3-11
Zhou T, Dong Y L, Huo B Q, Liu S and Ma Z J. 2021. Overview of the application of u-net network in medical image segmentation. Journal of Image and Graphics, 26 (9): 2058-2077
周涛, 董雅丽, 霍兵强, 刘珊, 马宗军. 2021. U-Net网络医学图像分割应用综述. 中国图象图形学报, 26(9): 2058-2077
Zhou T, Liu S, Dong Y L, Huo B Q and Ma Z J. 2021. Multiscale transform pixel level medical image fusion: research progress, applications and challenges. Journal of Image and Graphics, 26 (9): 2094-2110
周涛, 刘珊, 董雅丽, 霍兵强, 马宗军. 2021. 多尺度变换像素级医学图像融合: 研究进展、应用和挑战. 中国图象图形学报, 26(9): 2094-2110
Zhu W T, Liu C C, Fan W and Xie X H. 2018. DeepLung: deep 3D dual path nets for automated pulmonary nodule detection and classification//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Tahoe, USA: IEEE: 673-681[ DOI: 10.1109/WACV.2018.00079 http://dx.doi.org/10.1109/WACV.2018.00079 ]
Zuo W X, Zhou F Q, Li Z X and Wang L. 2019. Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access, 7: 32510-32521[DOI:10.1109/ACCESS.2019.2903587]
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