Deep learning approach for automatic segmentation of auricular acupoint divisions_Journal of Biomedical Engineering

Authors：

GAO Zhenyue ^1,2,3 , JIA Shijin ^1,3 , LI Qingfeng ⁴ , LU Dongxin ⁴ , ZHANG Sen ^1,2 ,  XIAO Wendong ^1,2,3

1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China;
2. Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology Beijing, Beijing 100083, P. R. China;
3. Shunde Innovation School, University of Science and Technology Beijing, Shunde, Guangdong 528399, P. R. China;
4. Mobile Health Management System Engineering Research Center of the Ministry of Education, Hangzhou Normal University, Hangzhou 311121, P. R. China;

Corresponding author：

XIAO Wendong, Email: wdxiao@ustb.edu.cn

Keywords：

Deep learning; Auricular acupoint therapy; Region segmentation; Image processing; Traditional Chinese medicine

DOI：

10.7507/1001-5515.202309010

Video：

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Abstract Full text Figures/Tables Video References Cited by

The automatic segmentation of auricular acupoint divisions is the basis for realizing intelligent auricular acupoint therapy. However, due to the large number of ear acupuncture areas and the lack of clear boundary, existing solutions face challenges in automatically segmenting auricular acupoints. Therefore, a fast and accurate automatic segmentation approach of auricular acupuncture divisions is needed. A deep learning-based approach for automatic segmentation of auricular acupoint divisions is proposed, which mainly includes three stages: ear contour detection, anatomical part segmentation and keypoints localization, and image post-processing. In the anatomical part segmentation and keypoints localization stages, K-YOLACT was proposed to improve operating efficiency. Experimental results showed that the proposed approach achieved automatic segmentation of 66 acupuncture points in the frontal image of the ear, and the segmentation effect was better than existing solutions. At the same time, the mean average precision (mAP) of the anatomical part segmentation of the K-YOLACT was 83.2%, mAP of keypoints localization was 98.1%, and the running speed was significantly improved. The implementation of this approach provides a reliable solution for the accurate segmentation of auricular point images, and provides strong technical support for the modern development of traditional Chinese medicine.

Citation： GAO Zhenyue, JIA Shijin, LI Qingfeng, LU Dongxin, ZHANG Sen, XIAO Wendong. Deep learning approach for automatic segmentation of auricular acupoint divisions. Journal of Biomedical Engineering, 2024, 41(1): 114-120. doi: 10.7507/1001-5515.202309010 Copy

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1. You E, Kim D, Harris R, et al. Effects of auricular acupressure on pain management: a systematic review. Pain Manag Nurs, 2019, 20(1): 17-24.
2. Huang C F, Guo S E, Chou F H. Auricular acupressure for overweight and obese individuals: A systematic review and meta-analysis. Medicine, 2019, 98(26): e16144.
3. Borlack R E, Shan S, Zong A M, et al. Electrodermal activity of auricular acupoints in pediatric patients with functional abdominal pain disorders. J Pediatr Gastr Nutr, 2021, 73(2): 184-191.
4. Wirz-Ridolfi A. The history of ear acupuncture and ear cartography: why precise mapping of auricular points is important. Med Acupunct, 2019, 31(3): 145-156.
5. Yang Y, Wen J, Hong J. The effects of auricular therapy for cancer pain: a systematic review and meta-analysis. Evid Based Complement Alternat Med, 2020, 2020: 1618767.
6. Liu M, Tong Y, Chai L, et al. Effects of auricular point acupressure on pain relief: a systematic review. Pain Manag Nurs, 2021, 22(3): 268-280.
7. Yeh C H, Huang L C. Comprehensive and systematic auricular diagnosis protocol. Med Acupunct, 2013, 25(6): 423-436.
8. Tournier J D, Smith R, Raffelt D, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage, 2019, 202: 116137.
9. Yadav S S, Jadhav S M. Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data, 2019, 6(1): 1-18.
10. Shen D, Wu G, Suk H I. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017, 19: 221-248.
11. 中华人民共和国国家质量监督检验检疫总局. GB/T13734—2008 耳穴名称与定位. 北京: 中国标准出版社, 2008.
12. Ao C, Jin S, Ding H, et al. Application and development of artificial intelligence and intelligent disease diagnosis. Curr Pharm Design, 2020, 26(26): 3069-3075.
13. Tang Y, Chen M, Wang C, et al. Recognition and localization methods for vision-based fruit picking robots: A review. Front Plant Sci, 2020, 11: 510.
14. Wu X, Sahoo D, Hoi S C H. Recent advances in deep learning for object detection. Neurocomputing, 2020, 396: 39-64.
15. Ahila Priyadharshini R, Arivazhagan S, Arun M. A deep learning approach for person identification using ear biometrics. Appl Intell, 2021, 51: 2161-2172.
16. Hassaballah M, Alshazly H A, Ali A A, Ear recognition using local binary patterns: A comparative experimental study. Expert Syst Appl, 2019, 118: 182-200.
17. Cintas C, Quinto-Sánchez M, Acuña V, et al. Automatic ear detection and feature extraction using geometric morphometrics and convolutional neural networks. IET Biometrics, 2017, 6(3): 211-223.
18. Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE T Pattern Anal, 2016, 39(6): 1137-1149.
19. Zhang Y, Mu Z, Yuan L, et al. USTB-Helloear: A large database of ear images photographed under uncontrolled conditions// 9th International Conference on Image and Graphics (ICIG). Shanghai: Springer, 2017: 405-416.
20. Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah: IEEE, 2018: 8759-8768.
21. Fu C Y, Shvets M, Berg A C. RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free. arXiv, 2019: 1901.03353.
22. Li Y, Qi H, Dai J, et al. Fully convolutional instance-aware semantic segmentation// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (ICCV). Venice: IEEE, 2017: 2359-2367.
23. He K, Gkioxari G, Dollár P, et al. Mask R-CNN// Proceedings of the IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2961-2969.
24. Wen J, Jiang M, Wang Y, et al. An auricular division method based on ASM algorithm. Technol Health Care, 2021, 29(S1): 487-495.

Journal of Biomedical Engineering

Deep learning approach for automatic segmentation of auricular acupoint divisions

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