Development of an abdominal acupoint localization system based on AI deep learning

ZHANG Mo; LI Yuming; SHI Zongming

doi:10.13703/j.0255-2930.20240207-0003

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Development of an abdominal acupoint localization system based on AI deep learning

Tool and Instrument | 更新时间：2025-03-13

- Development of an abdominal acupoint localization system based on AI deep learning
- Chinese Acupuncture & Moxibustion Vol. 45, Issue 3, Pages: 391-396(2025)
- 作者机构：
  
  北京大学第一医院中医、中西医结合科，北京 100034
  香港城市大学电子工程系，中国香港 201337
- 作者简介：
- 基金信息：
- DOI：10.13703/j.0255-2930.20240207-0003
  CLC：
- Received：07 February 2024，
  
  Online First：28 October 2024，
  
  Published：12 March 2025
- 稿件说明：
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ZHANG Mo, LI Yuming, SHI Zongming. Development of an abdominal acupoint localization system based on AI deep learning[J]. Chinese Acupuncture & Moxibustion, 2025, 45(3): 391-396.
DOI：

ZHANG Mo, LI Yuming, SHI Zongming. Development of an abdominal acupoint localization system based on AI deep learning[J]. Chinese Acupuncture & Moxibustion, 2025, 45(3): 391-396. DOI： 10.13703/j.0255-2930.20240207-0003.

摘要

本研究旨在开发一套基于计算机视觉和卷积神经网络（CNNs）的腹针穴位辅助定位系统。此系统针对腹部穴位定位的问题，构建并训练了一套多任务CNNs架构，分别定位出神阙穴及人体边界；利用网络定位到的神阙穴进一步推导出上脘、曲骨及双侧大横4个关键穴位特征点；利用仿射变换矩阵实现由图像空间坐标到穴位模板空间的精确映射，从而实现腹部穴位的精准定位。通过测试验证，该系统能够精确地在图像中识别并定位腹部的针灸穴位。此定位系统的研制为中医远程教育、辅助诊断治疗及智能针灸机器人等先进中医设备的研制提供了技术支撑，有助于推动中医针灸的标准化和智能化进程。

Abstract

This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization

a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8)

the system further deduces key characteristics of four acupoints: Shangwan (CV13)

Qugu (CV2)

and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space

achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education

diagnostic assistance

and advanced TCM equipment

such as intelligent acupuncture robots

facilitating the standardization and intelligent advancement of acupuncture.

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