Pulmonary nodule detection method with semantic feature score

doi:10.11772/j.issn.1001-9081.2019081335

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 925-930.DOI: 10.11772/j.issn.1001-9081.2019081335

• Frontier & interdisciplinary applications • Previous Articles

Pulmonary nodule detection method with semantic feature score

ZHANG Zhancheng¹, ZHANG Dalong¹, LUO Xiaoqing²

1. College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou Jiangsu 215009, China;
2. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China

Received:2019-08-05 Revised:2019-10-10 Online:2020-03-10 Published:2019-10-25
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61772237), the Fundamental Research Funds for the Central Universities (JUSRP51618B), the Technological Innovation Project of Major Industries in Suzhou City (SYG201702).

带有语义特征得分的肺结节检测方法

张战成¹, 张大龙¹, 罗晓清²

1. 苏州科技大学电子与信息工程学院, 江苏苏州 215009;
2. 江南大学物联网工程学院, 江苏无锡 214122

通讯作者: 张战成
作者简介:张战成(1977-),男,山西平遥人,副教授,博士,主要研究方向:图像融合、医学图像分析;张大龙(1994-),男,江苏宿迁人,硕士研究生,CCF会员,主要研究方向:目标检测、医学图像处理;罗晓清(1980-),女,江西南昌人,副教授,博士,CCF会员,主要研究方向:图像融合、医学图像分析。
基金资助:
国家自然科学基金资助项目（61772237）；中央高校基本科研业务费资助项目（JUSRP51618B）；苏州市重点产业技术创新项目（SYG201702）。

Abstract

Abstract: Since the results of existing intelligent algorithms of pulmonary nodule detection only predict the positions of nodules and cannot give semantical interpretations which are well known to doctors in clinical diagnosis, such as “lobulation”, “texture” and “spiculation”, a pulmonary nodule detection method with semantic feature score was proposed. Eight semantic features—subtlety, internal structure, lobulation, spiculation, margin, calcification, sphericity and texture were embedded into the Region Proposal Network (RPN) of Faster R-CNN, a new anchor box mechanism was designed, a fully connected network was added to realize the regression learning of semantic features, and the semantic scores were used as auxiliary information to realize the joint learning of pulmonary nodule detection and semantic prediction by training with Faster R-CNN. The proposed method was evaluated on the LIDC/IDRI dataset. Results show that the accuracy of pulmonary nodule localization is 91.2%, and the accuracy, sensitivity and specificity of benign and malignant classification are 81%, 91.2% and 70.8% respectively. On 8 semantic feature scores, the difference between doctors is 0.58±0.78 (mean absolute error±standard deviation), the proposed method achieves the difference of 0.62±1.03 with doctors, which is familiar to the former one. These results demonstrate that the modified network has good prediction accuracy and semantic feature prediction, and facilitates the understanding and clinical interpretations of machine prediction results for doctors.

Key words: pulmonary nodule detection, interpretability, Region Proposal Network （RPN）, semantic feature, joint training

摘要： 针对传统肺结节检测智能算法仅预测结节的位置，缺少医生临床习惯使用的“分叶”“纹理”“毛刺”等直观的语义解释的问题，提出一种带有语义特征得分的肺结节检测方法。将Faster R-CNN的区域建议网络（RPN）中嵌入细致度、内部结构、分叶、毛刺、边界、钙化、圆度、纹理等8个医生常用的语义特征，设计新的锚盒机制，增加全连接网络实现语义特征的回归学习，将其作为辅助信息结合Faster R-CNN训练实现肺结节检测和语义预测的联合学习。LIDC/IDRI数据集上的测试结果显示，所提方法的肺结节定位的精度为91.2%，肺结节良恶性分类的准确率、敏感性、特异性分别为81%、91.2%、70.8%。在8个语义特征得分上，医生间的差异为0.59±0.80（平均绝对误差±标准差），所提算法预测的语义解释和医生的差异为0.62±1.03，预测结果接近医生个体的差异。修改后的网络达到了较好的检测精度和语义特征预测，方便医生对机器检测结果的理解和临床解释。

关键词: 肺结节检测, 可解释性, 区域建议网络, 语义特征, 联合训练

CLC Number:

TP391.41

ZHANG Zhancheng, ZHANG Dalong, LUO Xiaoqing. Pulmonary nodule detection method with semantic feature score[J]. Journal of Computer Applications, 2020, 40(3): 925-930.

张战成, 张大龙, 罗晓清. 带有语义特征得分的肺结节检测方法[J]. 计算机应用, 2020, 40(3): 925-930.

References

[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 and Informatics,2015,4(1):27-40.
[2] ZHANG J,XIA Y,CUI H,et al. Pulmonary nodule detection in medical images:a survey[J]. Biomedical Signal Processing and Control,2018,43:138-147.
[3] AI MOHAMMAD B,HILLIS S L,REED W,et al. Radiologist performance in the detection of lung cancer using CT[J]. Clinical Radiology,2019,74(1):67-75.
[4] 韩光辉, 刘峡壁, 郑光远. 肺部CT图像病变区域检测方法[J]. 自动化学报,2017,43(12):2071-2090. (HAN G H,LIU X B, ZHENG G Y. Automated detection of lesion regions in lung computed tomography images:a review[J]. Acta Automatica Sinica, 2017,43(12):2071-2090.)
[5] LAKSHMI N A,JEEVA J B. A computer aided diagnosis for detection and classification of lung nodules[C]//Proceedings of the IEEE 9th International Conference on Intelligent Systems and Control. Piscataway:IEEE,2015:1-5.
[6] CHOI W J,CHOI T S. Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images[J]. Information Sciences, 2012,212:57-78.
[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] VAN GINNEKEN B,SETIO A A A,JACOBS C,et al. Off-theshelf convolutional neural network features for pulmonary nodule detection in computed tomography scans[C]//Proceedings of the IEEE 12th International Symposium on Biomedical Imaging. Piscataway:IEEE,2015:286-289.
[9] 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 and Optoelectronics Progress,2018,55(5):129-137.)
[10] CHEN S,QIN J,JI X,et al. Automatic scoring of multiple semantic attributes with multi-task feature leverage:a study on pulmonary nodules in CT images[J]. IEEE Transactions on Medical Imaging,2017,36(3):802-814.
[11] REN S,HE K,GIRSHICK R,et al. Faster R-CNN:towards realtime object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge:MIT Press,2016:91-99.
[12] LIU W,ANGUELOV D,ERHAN D,et al. SSD:single shot multibox detector[C]//Proceedings of the 2016 European Conference on Computer Vision,LNCS 9905. Cham:Springer,2016:21-37.
[13] DAI J,YI L,HE K,et al. R-FCN:object detection via regionbased fully convolutional networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. New York:Curran Associates Inc.,2016:379-387.
[14] SZEGEDY C,IOFFE S,VANHOUCKE V,et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2017:4278-4285.
[15] HUANG J,RATHOD V,CHEN S,et al. Speed/accuracy tradeoffs for modern convolutional object detectors[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2017:3296-3297.
[16] HE K,ZHANG X,REN S,et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778.
[17] SZEGEDY C,LIU W,JIA Y,et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2015:1-9.
[18] OLIVEIRA G L,VALADA A,BOLLEN C,et al. Deep learning for human part discovery in images[C]//Proceedings of the 2016 IEEE International Conference on Robotics and Automation. Piscataway:IEEE,2016:1634-1641.
[19] ARMATO S G III,MCLENNAN G,BIDAUT L,et al. The Lung Image Database Consortium(LIDC)and Image Database Resource Initiative(IDRI):a completed reference database of lung nodules on CT scans[J]. Academic Radiology,2007,14(12):1455-1463.
[20] MCNITT-GRAY M F,ARMATO S G III,MEYER C R,et al. The Lung Image Database Consortium(LIDC)data collection process for nodule detection and annotation[J]. Academic Radiology, 2007,14(12):1464-1474.
[21] 林岚, 吴玉超, 宋爽, 等. LIDC/IDRI影像数据库在肺结节计算机辅助诊断中的研究进展[J]. 医疗卫生装备,2018,39(10):95-99.(LIN L,WU Y C,SONG S,et al. Lung Image Database Consortium and Image Database Resource Initiative:reviewing progresses toward improved computer-aided diagnosis of pulmonary nodule[J]. Chinese Medical Equipment Journal,2018,39(10):95-99.)
[22] CHEN S,NI D,QIN J,et al. Bridging computational features toward multiple semantic features with multi-task regression:a study of CT pulmonary nodules[C]//Proceedings of the 2016 International Conference on Medical Image Computing and Computer-Assisted Intervention,LNCS 9901. Cham:Springer,2016:53-60.
[23] 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[D]. 深圳:深圳大学,2017:35. (CHEN S H. Automatic rating of semantic attributes based on multi-task features in CT images of pulmonary nodules[D]. Shenzhen:Shenzhen University, 2017:35.)
[24] SAIEN S,MOGHADDAM H A,FATHIAN M. A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection[J]. International Journal of Computer Assisted Radiology and Surgery,2018,13(3):397-409.

Pulmonary nodule detection method with semantic feature score

带有语义特征得分的肺结节检测方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	ZHOU Xianbing, FAN Xiaochao, REN Ge, YANG Yong. Automated English essay scoring method based on multi-level semantic features [J]. Journal of Computer Applications, 2021, 41(8): 2205-2211.
[2]	LUO Hui, JIA Chen, LU Chunyu, LI Jian. Rail tread block defects detection method based on improved Faster R-CNN [J]. Journal of Computer Applications, 2021, 41(3): 904-910.
[3]	TAN Jinyuan, DIAO Yufeng, QI Ruihua, LIN Hongfei. Automatic summary generation of Chinese news text based on BERT-PGN model [J]. Journal of Computer Applications, 2021, 41(1): 127-132.
[4]	GAO Zhiyong, HUANG Jinzhen, DU Chenggang. Pulmonary nodule detection based on feature pyramid networks [J]. Journal of Computer Applications, 2020, 40(9): 2571-2576.
[5]	SHI Lukui, MA Hongqi, ZHANG Chaozong, FAN Shiyan. Detection method of pulmonary nodules based on improved residual structure [J]. Journal of Computer Applications, 2020, 40(7): 2110-2116.
[6]	WU Yuxin, CAI Ting, ZHANG Dabin. Automatic smart contract classification model based on hierarchical attention mechanism and bidirectional long short-term memory neural network [J]. Journal of Computer Applications, 2020, 40(4): 978-984.
[7]	QI Yongjun, GU Junhua, ZHANG Yajuan, WANG Feng, TIAN Zepei. Deep mixed convolution model for pulmonary nodule detection [J]. Journal of Computer Applications, 2020, 40(10): 2904-2909.
[8]	SHI Guoqiang, ZHAO Xia. Object tracking algorithm based on jointly-optimized strong-coupled Siamese region proposal network [J]. Journal of Computer Applications, 2020, 40(10): 2822-2830.
[9]	ZHAO Hong, WANG Le, WANG Weijie. Text sentiment analysis based on serial hybrid model of bi-directional long short-term memory and convolutional neural network [J]. Journal of Computer Applications, 2020, 40(1): 16-22.
[10]	DENG Zhonghao, CHEN Xiaodong. Pulmonary nodule detection algorithm based on deep convolutional neural network [J]. Journal of Computer Applications, 2019, 39(7): 2109-2115.
[11]	LIU Chengbin, ZHENG Wei, FAN Xin, YANG Fengyu. Hybrid defect prediction model based on network representation learning [J]. Journal of Computer Applications, 2019, 39(12): 3633-3638.
[12]	ZHI Shuting, LI Xiaoge, WANG Jingbo, WANG Penghua. Sentiment analysis of entity aspects based on multi-attention long short-term memory [J]. Journal of Computer Applications, 2019, 39(1): 160-167.
[13]	QIU Qingyu, LI Jing, QUAN Bing, TONG Chao, ZHANG Lijun, ZHANG Haixian. Similarity search based on semantic features of bibliographic information network [J]. Journal of Computer Applications, 2018, 38(5): 1327-1333.
[14]	LEI Hengxin, LIU Jinglei. Improving feature selection and matrix recovery ability by CUR matrix decomposition [J]. Journal of Computer Applications, 2017, 37(3): 640-646.
[15]	DONG Linjia, QIANG Yan, ZHAO Juanjuan, YUAN jie, ZHAO Wenting. Automatic detection of pulmonary nodules based on 3D shape index [J]. Journal of Computer Applications, 2017, 37(11): 3182-3187.