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.
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。.
Keywords: Auricular acupoint therapy; Deep learning; Image processing; Region segmentation; Traditional Chinese medicine.