ArticlePloS one2022
Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast.Npj imaging · 2026Review
- Automated artificial intelligence-driven 3-dimensional craniofacial superimposition for clinically useful treatment outcomes in growing patients: A multicenter study.American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2026Article
- Enhancing airway obstruction diagnosis with multimodal 3D shape analysis.International journal of computer assisted radiology and surgery · 2026Article
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Computational Insights into Root Canal Treatment: A Survey of Selected Methods in Imaging, Segmentation, Morphological Analysis, and Clinical Management.Dentistry journal · 2025Review
- Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry.Orthodontics & craniofacial research · 2025Article
- Aligning MRI and CBCT for Advanced TMJ Diagnostics: Case Series Using AI-Powered Registration in Dentistry and Orthodontics.Seminars in orthodontics · 2025Article
- A comparative three-dimensional analysis of skeletal and dental changes induced by Herbst and PowerScope appliances in Class II malocclusion treatment: a retrospective cohort study.Progress in orthodontics · 2025Article
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Automated Cone Beam Computed Tomography Segmentation of Multiple Impacted Teeth With or Without Association to Rare Diseases: Evaluation of Four Deep Learning-Based Methods.Orthodontics & craniofacial research · 2025Article
- Deep Learning-Based Three-Dimensional Analysis Reveals Distinct Patterns of Condylar Remodelling After Orthognathic Surgery in Skeletal Class III Patients.Orthodontics & craniofacial research · 2025Article
- Explainable artificial intelligence to quantify adenoid hypertrophy-related upper airway obstruction using 3D Shape Analysis.Journal of dentistry · 2025Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Segmenting Based on UNETR Network and 3D Reconstruction of Interventricular Septal-Free Wall Structure.Applied bionics and biomechanics · 2025Article
- Maxillary protraction anchored on miniplates versus miniscrews: three-dimensional dentoskeletal comparison.European journal of orthodontics · 2024Article
- The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.Cancers · 2024Review
- Accuracy Assessment of EM3D App-Based 3D Facial Scanning Compared to Cone Beam Computed Tomography.Dentistry journal · 2024Article
- An automated method for assessing condyle head changes in patients with skeletal class II malocclusion based on Cone-beam CT images.Dento maxillo facial radiology · 2024Article
- Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery.Orthodontics & craniofacial research · 2024Article
Corrections and comments
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Authors and funding
29 authors.
Funding
Abstract
The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.
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Registered trials
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.