ArticlePeerJ. Computer science2024
Semi or fully automatic tooth segmentation in CBCT images: a review.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- Exploring Technological Progress in Three-Dimensional Imaging for Root Canal Treatments: A Systematic Review.International dental journal · 2025Pooled it
- CBCT in Dental Research: Is Complexity Necessary? An Exploratory Proof-of-Concept Study.Journal of functional biomaterials · 2026Article
- Performance assessment of five artificial intelligence-based algorithms for automated tooth segmentation and labeling on intraoral scans.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- LADAS: A Localization-Adaptive Dual-Branch Framework for Accurate Delineation of Teeth and Jaw Bone in Axial CBCT Slices.Journal of imaging informatics in medicine · 2026Article
- The Use of Artificial Intelligence in Planning Dental Implant Procedures: A Systematic Review.Dentistry journal · 2026Review
- Development of Semi-Automatic Dental Image Segmentation Workflows with Root Canal Recognition for Faster Ground Tooth Acquisition.Journal of imaging · 2025Article
- Accuracy and reliability of a three-dimensional superimposition method for maxillary jaws and dentition.BMC oral health · 2025Article
- The Integration of Micro-CT Imaging and Finite Element Simulations for Modelling Tooth-Inlay Systems for Mechanical Stress Analysis: A Preliminary Study.Journal of functional biomaterials · 2025Article
- Assessing the accuracy of artificial intelligence in mandibular canal segmentation compared to semi-automatic segmentation on cone-beam computed tomography images.Polish journal of radiology · 2025Article
- Evaluation of CBCT reconstructed tooth models at different thresholds and voxels and their accuracy in fusion with IOS data: an in vitro validation study.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.
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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.