摘要
耳部穴区的自动分割是实现智能化耳穴疗法的基础。然而,由于耳部穴区较多,且缺乏清晰的边界特征,现有方案在自动分割耳穴时面临着挑战。因此,需要一种快速准确的耳部穴区自动分割方法。本研究提出了一种基于深度学习的耳部穴区自动分割方法,主要包含耳部轮廓检测、解剖部位分割及关键点估计和图像后处理三个阶段。本文还提出了K-YOLACT以提升解剖部位分割及关键点定位的运行效率。实验结果表明,所提出的方法实现了对耳部正面图像内66个穴区的自动分割,分割效果优于现有方案。同时K-YOLACT方法的解剖部位分割的平均精度均值(mAP)为83.2%,关键点定位平均精度均值为98.1%,且运行效率明显提升。该方法的提出为耳穴图像的精确分割提供了可靠的解决方案,也为中医疗法的现代化发展提供了强有力的技术支持。
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.
作者
高枕岳
贾世瑾
李青峰
芦东昕
张森
肖文栋
GAO Zhenyue;JIA Shijin;LI Qingfeng;LU Dongxin;ZHANG Sen;XIAO Wendong(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,P.R.China;Beijing Engineering Research Center of Industrial Spectrum Imaging,University of Science and Technology Beijing,Beijing 100083,P.R.China;Shunde Innovation School,University of Science and Technology Beijing,Shunde,Guangdong 528399,P.R.China;Mobile Health Management System Engineering Research Center of the Ministry of Education,Hangzhou Normal University,Hangzhou 311121,P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
北大核心
2024年第1期114-120,共7页
Journal of Biomedical Engineering
基金
国家自然科学基金项目(62173032)
佛山市科技创新专项资金项目(BK22BF005)
广东省基础与应用基础研究基金区域联合基金项目(2022A1515140109)。
关键词
深度学习
耳穴疗法
区域分割
图像处理
中医
Deep learning
Auricular acupoint therapy
Region segmentation
Image processing
Traditional Chinese medicine
