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[Deep learning approach for automatic segmentation of auricular acupoint divisions].

Zhenyue Gao^1,2,3, Shijin Jia^1,3, Qingfeng Li⁴

¹School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|February 25, 2024

Summary

This study introduces a deep learning method for automatically segmenting auricular acupoints, improving accuracy and speed for intelligent auricular therapy. The approach enhances traditional Chinese medicine applications by precisely identifying 66 ear acupuncture points.

Keywords:

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

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Area of Science:

Medical Imaging
Traditional Chinese Medicine
Deep Learning

Context:

Intelligent auricular acupoint therapy relies on accurate segmentation of ear acupuncture points.
Existing methods struggle with the numerous ear acupuncture areas and indistinct boundaries.
A need exists for efficient and precise automatic segmentation of auricular acupoint divisions.

Purpose:

To propose a deep learning-based approach for automatic segmentation of auricular acupoint divisions.
To enhance operating efficiency in anatomical part segmentation and keypoints localization using K-YOLACT.
To achieve accurate segmentation of 66 auricular acupuncture points in frontal ear images.

Summary:

A three-stage deep learning approach is presented: ear contour detection, anatomical part segmentation and keypoints localization (using K-YOLACT), and image post-processing.
The K-YOLACT model achieved a mean average precision (mAP) of 83.2% for anatomical part segmentation and 98.1% for keypoints localization.
Experimental results demonstrate superior segmentation accuracy and significantly improved running speed compared to existing solutions.

Impact:

Provides a reliable solution for accurate segmentation of auricular point images.
Offers strong technical support for the modernization and development of traditional Chinese medicine.
Facilitates the advancement of intelligent auricular acupoint therapy through precise segmentation.