ArticleIEEE transactions on medical imaging2021
Fast and Accurate Craniomaxillofacial Landmark Detection via 3D Faster R-CNN.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.La Radiologia medica · 2023Pooled it
- 3D ultrasound shape completion and anatomical feature detection for minimally invasive spine surgery.Medical & biological engineering & computing · 2026Article
- Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review.Frontiers in dental medicine · 2026Review
- Attention-Base deep learning for 3D craniofacial soft tissue landmark detection and diagnosis in orthodontics.Scientific reports · 2025Article
- Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.BMC oral health · 2025Article
- Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry.Orthodontics & craniofacial research · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair.Bioengineering (Basel, Switzerland) · 2025Article
- Basketball detection based on YOLOv8.PloS one · 2025Article
- Multi-Scale 3D Cephalometric Landmark Detection Based on Direct Regression with 3D CNN Architectures.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.PloS one · 2024Article
- Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.Computer methods and programs in biomedicine · 2023Article
- The Reliability of Three-Dimensional Landmark-Based Craniomaxillofacial and Airway Cephalometric Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- The Application of Deep Learning on CBCT in Dentistry.Diagnostics (Basel, Switzerland) · 2023Review
- Relational reasoning network for anatomical landmarking.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Contemporary Role and Applications of Artificial Intelligence in Dentistry.F1000Research · 2023Review
- Preparing computed tomography images for machine learning in forensic and virtual anthropology.Forensic science international. Synergy · 2023Article
- Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy.Healthcare (Basel, Switzerland) · 2021Article
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Authors and funding
10 authors.
Funding
Abstract
Automatic craniomaxillofacial (CMF) landmark localization from cone-beam computed tomography (CBCT) images is challenging, considering that 1) the number of landmarks in the images may change due to varying deformities and traumatic defects, and 2) the CBCT images used in clinical practice are typically large. In this paper, we propose a two-stage, coarse-to-fine deep learning method to tackle these challenges with both speed and accuracy in mind. Specifically, we first use a 3D faster R-CNN to roughly locate landmarks in down-sampled CBCT images that have varying numbers of landmarks. By converting the landmark point detection problem to a generic object detection problem, our 3D faster R-CNN is formulated to detect virtual, fixed-size objects in small boxes with centers indicating the approximate locations of the landmarks. Based on the rough landmark locations, we then crop 3D patches from the high-resolution images and send them to a multi-scale UNet for the regression of heatmaps, from which the refined landmark locations are finally derived. We evaluated the proposed approach by detecting up to 18 landmarks on a real clinical dataset of CMF CBCT images with various conditions. Experiments show that our approach achieves state-of-the-art accuracy of 0.89 ± 0.64mm in an average time of 26.2 seconds per volume.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.