Multi-View Deep Learning for Mandibular Landmark Localization

Zixiang Gao, Jing Wang, Zichong An, Yujia Zhu, Aonan Wen, Xiangling Fu, Yong Wang, Yijiao Zhao,
Multi-View Deep Learning for Mandibular Landmark Localization,
Journal of Dentistry,
2025,
106295,
ISSN 0300-5712,
https://doi.org/10.1016/j.jdent.2025.106295.
(https://www.sciencedirect.com/science/article/pii/S0300571225007389)
Abstract: Objectives
Accurate localization of anatomical landmarks on the mandible is crucial for maxillofacial surgery and orthodontic treatment planning. This study aims to develop and validate a novel multi-view deep learning framework to enhance the accuracy and efficiency of landmark localization on CBCT-derived 3D mandibular surface models.
Methods
We propose a multi-view stacked hourglass convolutional neural network (MVSH-CNN) that localizes 19 anatomical landmarks on 3D mandibular surface models reconstructed from cone beam computed tomography (CBCT) scans. A total of 140 mandibular scans from adult Han Chinese individuals were used, with 100 cases for training/validation and 40 cases (20 normal, 20 asymmetry) for independent testing. Manual annotations served as the reference standard. Localization performance was compared with the MeshMonk non-rigid registration method using Euclidean distance error and computational time.
Results
MVSH-CNN achieved a mean localization error of 1.13 ± 0.85 mm in the normal group and 1.10 ± 0.79 mm in the asymmetry group, significantly outperforming MeshMonk (1.42 ± 1.28 mm and 1.43 ± 1.14 mm, respectively; P < 0.05). Processing time per mandible was reduced from 356 seconds to 19.65 seconds. Over 94% of landmarks localized by MVSH-CNN had an error < 2 mm, meeting the predefined clinical threshold.
Conclusions
The MVSH-CNN framework provides accurate, robust, and time-efficient semi-automated 3D landmark localization directly on STL-based mandibular models, outperforming conventional registration-based approaches.
Clinical significance
MVSH-CNN offers a semi-automated and clinically viable solution for digital orthodontic assessment, virtual surgical planning, and intelligent craniofacial analysis, significantly reducing manual workload while enhancing reproducibility and standardization.
Keywords: Imaging, Three-Dimensional; Automatic landmarks; Mandible; Detection Algorithms; Artificial Intelligence; Deep Learning; Dentofacial Deformities