改进U型残差网络用于肺结节检测

doi:10.3778/j.issn.1002-8331.2011-0211

摘要/Abstract

摘要： 针对计算机断层扫描（CT）影像中肺结节检测灵敏度较低，且存在大量假阳性的问题，提出一种改进的U型残差网络用于肺结节检测。采取U-net网络的U型结构并利用残差学习方式构建深层次网络，同时引入自校正卷积增加特征的信息提取能力，进行通道间与局部信息增强，有利于检测不同形态的结节；通过引入的通道注意力机制，对特征提取过程中的特征进行重标定，实现自适应学习特征权重，进一步提高检测的准确率；引入DR loss作为该算法的分类损失函数，用于解决数据正负样本失衡问题。在LUNA16数据集对所提算法进行了验证，CPM得分达到0.901，提高了肺结节检测的灵敏度，而且有效降低了检测结果的平均假阳性个数，可有效辅助放射科医师对肺结节进行检测。

关键词: 图像处理, 肺结节检测, 自校正卷积, 注意力机制, 特征提取

Abstract: Aiming at the low sensitivity of lung nodule detection in computed tomography（CT） images, and there are a large number of false positives, an improved U-shaped residual network is proposed for lung nodule detection. Firstly, the U-shaped structure of U-net network and residual learning method are used to construct deep-seated network, At the same time, self-calibrated convolutions is introduced to enhance the information extraction ability of features and enhance the inter channel and local information, which is conducive to the detection of different shapes of nodules. Secondly, through the introduction of channel attention mechanism, the features in the feature extraction process are recalibrated to realize adaptive learning feature weight. In addition, DR loss is introduced as the classification loss function of this algorithm to solve the imbalance problem of positive and negative samples. The proposed algorithm is validated in LUNA16 data set, and the CPM score reaches 0.901, which improves the sensitivity of pulmonary nodule detection, and effectively reduces the average number of false positive results, which can effectively assist radiologists to detect pulmonary nodules.

Key words: image processing, pulmonary nodule detection, self-calibrated convolution, attention mechanism, feature extraction

袁金丽, 赵琳琳, 郭志涛, 苏逸, 卢成钢. 改进U型残差网络用于肺结节检测[J]. 计算机工程与应用, 2022, 58(13): 195-203.

YUAN Jinli, ZHAO Linlin, GUO Zhitao, SU Yi, LU Chenggang. Improved U-Shaped Residual Network for Lung Nodule Detection[J]. Computer Engineering and Applications, 2022, 58(13): 195-203.

参考文献

[1] BHAVANISHANKAR K，SUDHAMANI M V.Techniques for detection of solitary pulmonary nodules in human lung and their classifications—A survey[J].International Journal on Cybernetics & Informatics，2015，4（1）：27-40.
[2] 王昌淼.基于胸部影像的肺结节检测与分类关键技术研究[D].深圳：中国科学院大学，2018：1-3.
WANG C M.key techniques for lung nodules detection and classification based on chest imaging[D].Shenzhen：University of Chinese Academy of Sciences，2018：1-3.
[3] 陈首英，刘福林，庞志刚，等.肺癌病人5年生存率及生存因素分析[J].预防医学论坛，2004，10（1）：1-3.
CHEN S Y，LIU F L，PANG Z G，et al.A analysis of 5-year survival rate and survival factors of lung cancer patients[J].Forum on Preventive Medicine，2004，10（1）：1-3.
[4] SLUIMER I，SCHILHAM A，PROKOP M，et al.Computer analysis of computed omtography scans of the lung：A survey[J].IEEE Transactions Medical Imaging，2006，25（4）：385-405.
[5] 钟思华，郭兴明，郑伊能.改进U-Net网络的肺结节分割方法[J].计算机工程与应用，2020，56（17）：203-209.
ZHONG S H，GUO X M，ZHENG Y N.Improved U-Net network method of lung nodule segmentation[J].Computer Engineering and Applications，2020，56（17）：203-209.
[6] 赵清一，孔平，闵建中，等.肺结节检测与分类的深度学习方法综述[J].生物医学工程学杂志，2019，36（6）：1060-1068.
ZHAO Q Y，KONG P，MIN J Z，et al.A review of deep learning methods for the detection and classification of pulmonary nodules[J].Journal of Biomedical Engineering，2019，36（6）：1060-1068.
[7] SETIO A A A，CIOMPI F，LITJENS G，et al.Pulmonary nodule detection in CT images：False positive reduction using multi-view convolutional networks[J].IEEE Transactions on Medical Imaging，2016，35（5）：1160-1169.
[8] 苗光，李朝锋.二维和三维卷积神经网络相结合的CT图像肺结节检测方法[J].激光与光电子学进展，2018，55（5）：129-137.
MIAO?G，LI?C?F.Detection of pulmonary nodules CT images combined?with?two-dimensional?and?three-dimensional?convolution?neural?networks[J].Laser?& Optoelectronics?Progress，2018，55（5）：129-137.
[9] 杨帆，谢红薇，刘爱媛.基于卷积神经网络的肺结节分类算法[J].计算机工程与应用，2019，55（7）：145-150.
YANG F，XIE H W，LIU A Y.Lung nodule classification algorithm based on convolutional neural network[J].Computer Engineering and Applications，2019，55（7）：145-150.
[10] FURST J D，RAICU D S，MAHESH S，et al.Lung nodule detection from CT scans using 3D convolutional neural networks without candidate selection[C]//Proceedings of Computer-Aided Diagnosis Conference，2018.
[11] HUANG X J，SHAN J J，VAIDYA V.Lung nodule detection in CT using 3D convolutional neural networks[C]//Proceedings of IEEE 14th International Symposium on Biomedical Imaging（ISBI），Melbourne，VIC，Australia，2017：379-383.
[12] 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（7）：1558-1567.
[13] PEZESHK A，HAMIDIAN S，PETRICK N，et al.3D convolutional neural networks for automatic detection of pulmonary nodules in chest CT[J].IEEE Journal of Biomedical and Health Informatics，2019，23（5）：2080-2090.
[14] JIN H，LI Z，TONG R F，et al.A deep 3D residual CNN for false?positive reduction in pulmonary nodule detection[J].Medical Physics，2018，45（5）：2097-2107.
[15] ZHU W，LIU C，FAN W，et al.DeepLung：3D deep convolutional nets for automated pulmonary nodule detection and classification[C]//Proceedings of IEEE Winter Conference on Applications of Computer Vision（WACV），March 12-15，2018：673-681.
[16] LI Y，FAN Y.DeepSEED：3D squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection[C]//Proceedings of IEEE 17th International Symposium on Biomedical Imaging（ISBI），April 3-7，2020：1866-1869.
[17] 黄盛，李菲菲，陈虬.基于改进深度残差网络的计算断层扫描图像分类算法[J].光学学报，2020，40（3）：50-58.
HUANG S，LI F F，CHEN Q.Computed tomography image classification algorithm based on improved deep residual network[J].Acta Optica Sinica，2020，40（3）：50-58.
[18] QIAN Q，CHEN L，LI H，et al.DR loss：Improving object detection by distributional ranking[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR），June 13-19，2020：12161-12169.
[19] LIU J J，HOU Q，CHENG M M，et al.Improving convolutional networks with self-calibrated convolutions[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR），June 13-19，2020：10093-10102.
[20] WANG Q，WU B，ZHU P，et al.ECA-net：Efficient channel attention for deep convolutional neural networks[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR），June 13-19，2020：11531-11539.
[21] HU J，SHEN L，SUN G，et al.Squeeze-and-excitation networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition，June 18-23，2018：7132-7141
[22] MESSAY T，HARDIE R C，TUINSTRA T R.Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset[J].Medical Image Analysis，2015，22（1）：48-62.
[23] 冯雨，易本顺，吴晨玥，等.基于三维卷积神经网络的肺结节识别研究[J].光学学报，2019，39（6）：248-253.
FENG Y，YI B S，WU C Y，et al.Pulmonary nodule recognition based on three-dimensional convolution neural network[J].Acta Optica Sinica，2019，39（6）：248-253.