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

Sha Su; Xueting Jia; Liping Zhan; Siyuan Gao; Qing Zhang; Xiaofeng Huang

doi:10.1016/j.heliyon.2024.e24097

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

Heliyon. 2024 Jan 9;10(2):e24097. doi: 10.1016/j.heliyon.2024.e24097. eCollection 2024 Jan 30.

Authors

Sha Su¹, Xueting Jia¹, Liping Zhan¹, Siyuan Gao¹, Qing Zhang¹, Xiaofeng Huang¹

Affiliation

¹ Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Abstract

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.

Keywords: Cone-beam computed tomography (CBCT); Convolutional neural networks (CNNs); Deep learning (DL); Instance segmentation; Mask R-CNN; Periodontal ligament (PDL).