Automatic segmentation and landmark detection of 3D CBCT images using semi supervised learning for assisting orthognathic surgery planning
Haomin Tang, Shu Liu, Yongxin Shi, Jin Wei, Juxiang Peng, Hongchao Feng
Abstract
Patients with abnormal relative position of the upper and lower jaws (the main part of the facial bones) require orthognathic surgery to improve the occlusal relationship and facial appearance. However, in addition to the retraction and protrusion of the maxillomandibular advancement, these patients may also develop asymmetry. This study aims to use a semi-supervised learning method to demonstrate the maxillary and mandible retraction, protrudation and asymmetry of patients before orthognathic surgery through automatic segmentation of 3D cone beam computed tomography (CBCT) images and landmark detection, so as to provide help for the preoperative planning of orthognathic surgery. Among them, the dice of the semi-supervised algorithm adopted in this study reached 93.41 and 96.89% in maxillary and mandibular segmentation tasks, and the average error of landmark detection tasks reached 1.908 ± 1.166 mm, both of which were superior to the full-supervised algorithm with the same data volume annotation. Therefore, we propose that the method can be applied in a clinical setting to assist surgeons in preoperative planning for orthognathic surgery.