Journal of dentistry · 2025
Multi-view deep learning for mandibular landmark localization.
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Gao Zixiang, Wang Jing, An Zichong, Zhu Yujia, Wen Aonan, Fu Xiangling, Wang Yong, Zhao Yijiao
Аннотация
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 s to 19.65 s. 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.
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 s to 19.65 s. 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.