Automatic recognition of cephalometric landmarks via multi-scale sampling strategy

• Published in: Heliyon 2023-06, Vol.9 (6), p.e17459-e17459, Article e17459
• Main Authors: Zhao, Congyi, Yuan, Zengbei, Luo, Shichang, Wang, Wenjie, Ren, Zhe, Yao, Xufeng, Wu, Tao
• Format: Article
• Language: English
• Subjects: algorithms; automatic detection; Automatic recognition; Cephalometric landmarks; Deep learning; head; Orthodontics; surgery
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2023.e17459

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.