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：

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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

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8.	徐勇, 張慧. 圖像自動標注方法研究綜述. 現代情報, 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.
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14.	卞蓉蓉, 李堯, 趙敏, 等. 101 例醫療器械形成異物遺留患者體內的不良事件. 醫療裝備, 2018, 31(17): 49-51.
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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.
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23.	姚鋒. 不同體位鎖骨下靜脈穿刺置入長期導管術的臨床應用. 貴州醫藥, 2015, 39(3): 232-233.
24.	康陽, 謝昕, 禹健, 賈敏. 2 例鎖骨骨折漏診原因分析及多體位投照方法價值. 醫學理論與實踐, 2015, 28(5): 576.
25.	葉興強, 張磊, 鄭放超, 等. 站立后前位胸片擺位方法的改良. 醫療衛生裝備, 2013, 34(8): 73-75.
26.	譚利, 李彬, 田聯房. 新的連通域標記方法及其在醫學圖像中的應用. 計算機應用研究, 2010, 27(11): 4338-4340.
27.	楊江茂, 李沁怡. 胸部后前位攝片中三種上肢擺位對肩胛骨的觀察. 中國誤診學雜志, 2009, 9(13): 3272.
28.	陳瀟君, 孫炳偉, 茍建平. 深度機器學習輔助醫院智能化管理. 中國現代醫學雜志, 2018, 28(8): 125-128.
29.	孟令, 朱正, 林世忠, 晏節晉. 淺談深度學習在圖像識別領域的應用現狀與優勢. 電工技術, 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. 基于特征學習的圖像質量評價. 南京: 東南大學, 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: 武漢志誠時代文化發展有限公司, 2018: 4.
6. 肖優明. 深度學習網絡取得突破性進展. 檢察風云, 2018(5): 37.
7. 張莉. DR 攝影圖像質量控制與質量保證. 中國繼續醫學教育, 2017, 9(21): 63-65.
8. 徐勇, 張慧. 圖像自動標注方法研究綜述. 現代情報, 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. 張金戈, 彭婉琳, 夏春潮, 等. 提高大型醫院 CT 設備使用效率方法探討. 現代醫院管理, 2016, 14(6): 39-41.
14. 卞蓉蓉, 李堯, 趙敏, 等. 101 例醫療器械形成異物遺留患者體內的不良事件. 醫療裝備, 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. 黃賽. 基于深度學習的 MR 圖像肝臟腫瘤自動化分割方法的研究. 南京: 南京大學, 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. 王獻抗. 碳鋼/不銹鋼復合無縫鋼管穿孔及多道次軋制成型過程的研究. 天津: 天津理工大學, 2014.
23. 姚鋒. 不同體位鎖骨下靜脈穿刺置入長期導管術的臨床應用. 貴州醫藥, 2015, 39(3): 232-233.
24. 康陽, 謝昕, 禹健, 賈敏. 2 例鎖骨骨折漏診原因分析及多體位投照方法價值. 醫學理論與實踐, 2015, 28(5): 576.
25. 葉興強, 張磊, 鄭放超, 等. 站立后前位胸片擺位方法的改良. 醫療衛生裝備, 2013, 34(8): 73-75.
26. 譚利, 李彬, 田聯房. 新的連通域標記方法及其在醫學圖像中的應用. 計算機應用研究, 2010, 27(11): 4338-4340.
27. 楊江茂, 李沁怡. 胸部后前位攝片中三種上肢擺位對肩胛骨的觀察. 中國誤診學雜志, 2009, 9(13): 3272.
28. 陳瀟君, 孫炳偉, 茍建平. 深度機器學習輔助醫院智能化管理. 中國現代醫學雜志, 2018, 28(8): 125-128.
29. 孟令, 朱正, 林世忠, 晏節晉. 淺談深度學習在圖像識別領域的應用現狀與優勢. 電工技術, 2018(16): 145-146, 149.

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