Deep Learning-Based Auricular Point Localization for Auriculotherapy
Auriculotherapy is one of the main forms of treatment in Traditional Chinese Medicine, whose potential as an alternative medicine for both health evaluation and disease treatment has been reported in many cases. However, its efficacy highly relies on the accurate localization of auricular points, wh...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9921214/ |
| _version_ | 1828099548215508992 |
|---|---|
| author | Xiaoyan Sun Jiagang Dong Qingfeng Li Dongxin Lu Zhenming Yuan |
| author_facet | Xiaoyan Sun Jiagang Dong Qingfeng Li Dongxin Lu Zhenming Yuan |
| author_sort | Xiaoyan Sun |
| collection | DOAJ |
| description | Auriculotherapy is one of the main forms of treatment in Traditional Chinese Medicine, whose potential as an alternative medicine for both health evaluation and disease treatment has been reported in many cases. However, its efficacy highly relies on the accurate localization of auricular points, which are not easy to be remembered due to their complexity. To explore an efficient way of locating auricular points, this study proposed a deep learning-based method of automatically locating auricular points from auricular images. A self-collected dataset named EID was created for TCM auriculotherapy research, with 91 auriculotherapy-related landmark points manually annotated according to the Chinese national standardization. A deep neural network structure was trained for landmark detection, and a direction normalization module was proposed to compensate for the detection error caused by the difference between the left and right ears. The trained model was validated on dataset EID. An average NME of 0.0514±0.0023 was achieved, which outperformed similar works. In addition, a certain auricular area corresponding to the digestive system was segmented based on the localized landmarks, and the results were tested in real-time video streaming. The proposed work for both auricular landmark and area identification can be widely used in auriculotherapy education and applications. |
| first_indexed | 2024-04-11T08:16:32Z |
| format | Article |
| id | doaj.art-fda9a3dba17e41b5a15cfc25374b0b3f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T08:16:32Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fda9a3dba17e41b5a15cfc25374b0b3f2022-12-22T04:35:09ZengIEEEIEEE Access2169-35362022-01-011011289811290810.1109/ACCESS.2022.32151389921214Deep Learning-Based Auricular Point Localization for AuriculotherapyXiaoyan Sun0https://orcid.org/0000-0002-8781-5303Jiagang Dong1Qingfeng Li2Dongxin Lu3Zhenming Yuan4https://orcid.org/0000-0002-7255-2010School of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaEngineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou, ChinaEngineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaAuriculotherapy is one of the main forms of treatment in Traditional Chinese Medicine, whose potential as an alternative medicine for both health evaluation and disease treatment has been reported in many cases. However, its efficacy highly relies on the accurate localization of auricular points, which are not easy to be remembered due to their complexity. To explore an efficient way of locating auricular points, this study proposed a deep learning-based method of automatically locating auricular points from auricular images. A self-collected dataset named EID was created for TCM auriculotherapy research, with 91 auriculotherapy-related landmark points manually annotated according to the Chinese national standardization. A deep neural network structure was trained for landmark detection, and a direction normalization module was proposed to compensate for the detection error caused by the difference between the left and right ears. The trained model was validated on dataset EID. An average NME of 0.0514±0.0023 was achieved, which outperformed similar works. In addition, a certain auricular area corresponding to the digestive system was segmented based on the localized landmarks, and the results were tested in real-time video streaming. The proposed work for both auricular landmark and area identification can be widely used in auriculotherapy education and applications.https://ieeexplore.ieee.org/document/9921214/Auriculotherapydeep learninglandmark detection |
| spellingShingle | Xiaoyan Sun Jiagang Dong Qingfeng Li Dongxin Lu Zhenming Yuan Deep Learning-Based Auricular Point Localization for Auriculotherapy IEEE Access Auriculotherapy deep learning landmark detection |
| title | Deep Learning-Based Auricular Point Localization for Auriculotherapy |
| title_full | Deep Learning-Based Auricular Point Localization for Auriculotherapy |
| title_fullStr | Deep Learning-Based Auricular Point Localization for Auriculotherapy |
| title_full_unstemmed | Deep Learning-Based Auricular Point Localization for Auriculotherapy |
| title_short | Deep Learning-Based Auricular Point Localization for Auriculotherapy |
| title_sort | deep learning based auricular point localization for auriculotherapy |
| topic | Auriculotherapy deep learning landmark detection |
| url | https://ieeexplore.ieee.org/document/9921214/ |
| work_keys_str_mv | AT xiaoyansun deeplearningbasedauricularpointlocalizationforauriculotherapy AT jiagangdong deeplearningbasedauricularpointlocalizationforauriculotherapy AT qingfengli deeplearningbasedauricularpointlocalizationforauriculotherapy AT dongxinlu deeplearningbasedauricularpointlocalizationforauriculotherapy AT zhenmingyuan deeplearningbasedauricularpointlocalizationforauriculotherapy |