ArticleIEEE transactions on medical imaging2022
Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.European journal of orthodontics · 2024Pooled it
- Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.La Radiologia medica · 2023Pooled it
- Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review.Frontiers in dental medicine · 2026Review
- Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning.Scientific reports · 2025Article
- Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.BMC oral health · 2025Article
- Autonomous surgical planning of mandibular angle reduction based on anatomical landmarks and osteotomy plane detection.Scientific reports · 2025Article
- Application of deep learning in wound size measurement using fingernail as the reference.BMC medical informatics and decision making · 2024Article
- CBCT analysis of mandibular position and bilateral symmetry in adult female patients with a deep overbite before and after flat bite plate treatment: a retrospective study.European journal of medical research · 2024Article
- Craniomaxillofacial landmarks detection in CT scans with limited labeled data via semi-supervised learning.Heliyon · 2024Article
- A Proof of Concept: Optimized Jawbone-Reduction Model for Mandibular Fracture Surgery.Journal of imaging informatics in medicine · 2024Article
- Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.PloS one · 2024Article
- Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model.BMC oral health · 2023Article
- The Application of Deep Learning on CBCT in Dentistry.Diagnostics (Basel, Switzerland) · 2023Review
- Image generation technology for functional occlusal pits and fissures based on a conditional generative adversarial network.PloS one · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly learning the local geometrical relationships between the landmarks, our approach extends Mask R-CNN for end-to-end prediction of landmark locations. Specifically, we first apply a detection network on a down-sampled 3D image to leverage global contextual information to predict the approximate locations of the landmarks. We subsequently leverage local information provided by higher-resolution image patches to refine the landmark locations. On patients with varying non-syndromic jaw deformities, our method achieves an average detection accuracy of 1.38± 0.95mm, outperforming a related state-of-the-art method.
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