Automatic tooth periodontal ligament segmentation of cone beam computed tomography based on instance segmentation network.

Objective: The three-dimensional morphological structures of periodontal ligaments (PDLs) are important data for periodontal, orthodontic, prosthodontic, and implant interventions. This study aimed to employ a deep learning (DL) algorithm to segment the PDL automatically in cone-beam computed tomography (CBCT).
Method: This was a retrospective study. We randomly selected 389 patients and 1734 axial CBCT images from the CBCT database, and designed a fully automatic PDL segmentation computer-aided model based on instance segmentation Mask R-CNN network. The labels of the model training were 'teeth' and 'alveolar bone', and the 'PDL' is defined as the region where the 'teeth' and 'alveolar bone' overlap. The model's segmentation performance was evaluated using CBCT data from eight patients outside the database.
Results: Qualitative evaluation indicates that the PDL segmentation accuracy of incisors, canines, premolars, wisdom teeth, and implants reached 100%. The segmentation accuracy of molars was 96.4%. Quantitative evaluation indicates that the mIoU and mDSC of PDL segmentation were 0.667 ± 0.015 (>0.6) and 0.799 ± 0.015 (>0.7) respectively.
Conclusion: This study analysed a unique approach to AI-driven automatic segmentation of PDLs on CBCT imaging, possibly enabling chair-side measurements of PDLs to facilitate periodontists, orthodontists, prosthodontists, and implantologists in more efficient and accurate diagnosis and treatment planning.
Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Xiaofeng Huang reports financial support was provided by This work was supported by Clinical Technology Innovation Project of Beijing Hospital Management Center, China (XMLX202132). Xieting Jia reports financial support was provided by 10.13039/501100001809National Natural Science Foundation of China, China (No. 82101005).
(© 2024 The Authors. Published by Elsevier Ltd.)