Research and application of orthotopic DR chest radiograph quality control system based on artificial intelligence_Journal of Biomedical Engineering

Authors：

WANG Jiyuan ¹ ,  LI Zhenlin ² , PU Lixin ^1,3 , ZHANG Kai ² , LIU Xiumin ² , ZHOU Bin ³

1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China;
2. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;
3. Chengdu Jinpan Electronic Science and Technology Multimedia Technology Co., Ltd., Chengdu 611731, P.R.China;

Corresponding author：

LI Zhenlin, Email: lzlcd01@126.com

Keywords：

medical imaging; quality evaluation; deep learning; image segmentation; image classification

DOI：

10.7507/1001-5515.201904017

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

With the change of medical diagnosis and treatment mode, the quality of medical image directly affects the diagnosis and treatment of the disease for doctors. Therefore, realization of intelligent image quality control by computer will have a greater auxiliary effect on the radiographer’s filming work. In this paper, the research methods and applications of image segmentation model and image classification model in the field of deep learning and traditional image processing algorithm applied to medical image quality evaluation are described. The results demonstrate that deep learning algorithm is more accurate and efficient than the traditional image processing algorithm in the effective training of medical image big data, which explains the broad application prospect of deep learning in the medical field. This paper developed a set of intelligent quality control system for auxiliary filming, and successfully applied it to the Radiology Department of West China Hospital and other city and county hospitals, which effectively verified the feasibility and stability of the quality control system.

Citation： WANG Jiyuan, LI Zhenlin, PU Lixin, ZHANG Kai, LIU Xiumin, ZHOU Bin. Research and application of orthotopic DR chest radiograph quality control system based on artificial intelligence. Journal of Biomedical Engineering, 2020, 37(1): 158-168. doi: 10.7507/1001-5515.201904017 Copy

1.	Walczak A, Paczkowski M. Medical data preprocessing for increased selectivity of diagnosis. Bio-Algorithms and Med-Systems, 2016, 12(1): 3-46.
2.	Liu Yunfeng, Dong Xingtao, Peng Wei. Study on digital data processing techniques for 3D medical model. Bioinformatics and Biomedical Engineering (iCBBE), 2010: 4-7.
3.	Pan Wenwen. Image quality evaluation based on saliency and feature similarity// Proceedings of 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017). Chengdu: IEEE, 2017: 5-7.
4.	王志平. 基于特征学习的图像质量评价. 南京: 东南大学, 2017.
5.	Zhou Yanmei. A study of blended learning design to promote deep learning// Proceedings of 2018 International Conference on Management and Education, Humanities and Social Sciences (MEHSS 2018). Wuhan Zhicheng Times Cultural Development Co., Ltd: 武汉志诚时代文化发展有限公司, 2018: 4.
6.	肖优明. 深度学习网络取得突破性进展. 检察风云, 2018(5): 37.
7.	张莉. DR 摄影图像质量控制与质量保证. 中国继续医学教育, 2017, 9(21): 63-65.
8.	徐勇, 张慧. 图像自动标注方法研究综述. 现代情报, 2016, 36(3): 144-150.
9.	Fabijańska A. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network. Artif Intell Med, 2018, 88: 1-13.
10.	Sheng Lian, Luo Zhiming, Zhong Zhun, et al. Attention guided U-Net for accurate iris segmentation. J Vis Comm Image R, 2018, 56: 296-304.
11.	Duan Na, Liu Lizheng, Yu Xianjia, et al. Classification of multichannel surface-electromyography signals based on convolutional neural networks. Journal of Industrial Information Integration, 2019: 15.
12.	Gong Maoguo, Yang Hailun, Zhang Puzhao. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 2017: 129.
13.	张金戈, 彭婉琳, 夏春潮, 等. 提高大型医院 CT 设备使用效率方法探讨. 现代医院管理, 2016, 14(6): 39-41.
14.	卞蓉蓉, 李尧, 赵敏, 等. 101 例医疗器械形成异物遗留患者体内的不良事件. 医疗装备, 2018, 31(17): 49-51.
15.	Guo Xiaoguang, Ou Min, Yi Gang, et al. Correction between the morphology of acromion and acromial angle in Chinese population: A Study on 292 scapulas. BioMed Research International, 2018, 2018: 3125715.
16.	Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 256-284.
17.	He Kaiming, Gkioxari G, Dollár P, et al. Mask R-CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 964-987.
18.	Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. arXiv preprint ar Xiv: 1505.04597v1, 2015.
19.	黄赛. 基于深度学习的 MR 图像肝脏肿瘤自动化分割方法的研究. 南京: 南京大学, 2018.
20.	Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 67-105.
21.	McCauley B D, Chu A F. Leadless cardiac pacemakers: The next evolution in pacemaker technology. R I Med J (2013), 2017, 100(11): 31-34.
22.	王献抗. 碳钢/不锈钢复合无缝钢管穿孔及多道次轧制成型过程的研究. 天津: 天津理工大学, 2014.
23.	姚锋. 不同体位锁骨下静脉穿刺置入长期导管术的临床应用. 贵州医药, 2015, 39(3): 232-233.
24.	康阳, 谢昕, 禹健, 贾敏. 2 例锁骨骨折漏诊原因分析及多体位投照方法价值. 医学理论与实践, 2015, 28(5): 576.
25.	叶兴强, 张磊, 郑放超, 等. 站立后前位胸片摆位方法的改良. 医疗卫生装备, 2013, 34(8): 73-75.
26.	谭利, 李彬, 田联房. 新的连通域标记方法及其在医学图像中的应用. 计算机应用研究, 2010, 27(11): 4338-4340.
27.	杨江茂, 李沁怡. 胸部后前位摄片中三种上肢摆位对肩胛骨的观察. 中国误诊学杂志, 2009, 9(13): 3272.
28.	陈潇君, 孙炳伟, 苟建平. 深度机器学习辅助医院智能化管理. 中国现代医学杂志, 2018, 28(8): 125-128.
29.	孟令, 朱正, 林世忠, 晏节晋. 浅谈深度学习在图像识别领域的应用现状与优势. 电工技术, 2018(16): 145-146, 149.

1. Walczak A, Paczkowski M. Medical data preprocessing for increased selectivity of diagnosis. Bio-Algorithms and Med-Systems, 2016, 12(1): 3-46.
2. Liu Yunfeng, Dong Xingtao, Peng Wei. Study on digital data processing techniques for 3D medical model. Bioinformatics and Biomedical Engineering (iCBBE), 2010: 4-7.
3. Pan Wenwen. Image quality evaluation based on saliency and feature similarity// Proceedings of 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017). Chengdu: IEEE, 2017: 5-7.
4. 王志平. 基于特征学习的图像质量评价. 南京: 东南大学, 2017.
5. Zhou Yanmei. A study of blended learning design to promote deep learning// Proceedings of 2018 International Conference on Management and Education, Humanities and Social Sciences (MEHSS 2018). Wuhan Zhicheng Times Cultural Development Co., Ltd: 武汉志诚时代文化发展有限公司, 2018: 4.
6. 肖优明. 深度学习网络取得突破性进展. 检察风云, 2018(5): 37.
7. 张莉. DR 摄影图像质量控制与质量保证. 中国继续医学教育, 2017, 9(21): 63-65.
8. 徐勇, 张慧. 图像自动标注方法研究综述. 现代情报, 2016, 36(3): 144-150.
9. Fabijańska A. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network. Artif Intell Med, 2018, 88: 1-13.
10. Sheng Lian, Luo Zhiming, Zhong Zhun, et al. Attention guided U-Net for accurate iris segmentation. J Vis Comm Image R, 2018, 56: 296-304.
11. Duan Na, Liu Lizheng, Yu Xianjia, et al. Classification of multichannel surface-electromyography signals based on convolutional neural networks. Journal of Industrial Information Integration, 2019: 15.
12. Gong Maoguo, Yang Hailun, Zhang Puzhao. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 2017: 129.
13. 张金戈, 彭婉琳, 夏春潮, 等. 提高大型医院 CT 设备使用效率方法探讨. 现代医院管理, 2016, 14(6): 39-41.
14. 卞蓉蓉, 李尧, 赵敏, 等. 101 例医疗器械形成异物遗留患者体内的不良事件. 医疗装备, 2018, 31(17): 49-51.
15. Guo Xiaoguang, Ou Min, Yi Gang, et al. Correction between the morphology of acromion and acromial angle in Chinese population: A Study on 292 scapulas. BioMed Research International, 2018, 2018: 3125715.
16. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 256-284.
17. He Kaiming, Gkioxari G, Dollár P, et al. Mask R-CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 964-987.
18. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. arXiv preprint ar Xiv: 1505.04597v1, 2015.
19. 黄赛. 基于深度学习的 MR 图像肝脏肿瘤自动化分割方法的研究. 南京: 南京大学, 2018.
20. Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 67-105.
21. McCauley B D, Chu A F. Leadless cardiac pacemakers: The next evolution in pacemaker technology. R I Med J (2013), 2017, 100(11): 31-34.
22. 王献抗. 碳钢/不锈钢复合无缝钢管穿孔及多道次轧制成型过程的研究. 天津: 天津理工大学, 2014.
23. 姚锋. 不同体位锁骨下静脉穿刺置入长期导管术的临床应用. 贵州医药, 2015, 39(3): 232-233.
24. 康阳, 谢昕, 禹健, 贾敏. 2 例锁骨骨折漏诊原因分析及多体位投照方法价值. 医学理论与实践, 2015, 28(5): 576.
25. 叶兴强, 张磊, 郑放超, 等. 站立后前位胸片摆位方法的改良. 医疗卫生装备, 2013, 34(8): 73-75.
26. 谭利, 李彬, 田联房. 新的连通域标记方法及其在医学图像中的应用. 计算机应用研究, 2010, 27(11): 4338-4340.
27. 杨江茂, 李沁怡. 胸部后前位摄片中三种上肢摆位对肩胛骨的观察. 中国误诊学杂志, 2009, 9(13): 3272.
28. 陈潇君, 孙炳伟, 苟建平. 深度机器学习辅助医院智能化管理. 中国现代医学杂志, 2018, 28(8): 125-128.
29. 孟令, 朱正, 林世忠, 晏节晋. 浅谈深度学习在图像识别领域的应用现状与优势. 电工技术, 2018(16): 145-146, 149.

Previous Article
Research on adaptive pulse signal extraction algorithm based on fingertip video image
Next Article
Research advancements of motor imagery for motor function recovery after stroke

Journal of Biomedical Engineering

Research and application of orthotopic DR chest radiograph quality control system based on artificial intelligence

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content