ReviewJournal of personalized medicine2021
Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 60 citations in OpenAlex.
- Parotid gland tumor computed tomography image segmentation and benign-malignant differentiation diagnosis.Quantitative imaging in medicine and surgery · 2026Article
- AI-driven morphometric comparison of fibula vs. iliac flap in mandibular reconstruction.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Automated Global Positioning Layout (GPL) for accuracy assessment in CAD-CAM mandibular reconstruction - method introduction.Scientific reports · 2026Article
- A step-by-step guide for implementation of virtual surgical applications and patient data centralization: practical experience and lessons learned.3D printing in medicine · 2026Review
- Artificial Intelligence-enabled Automatic Computed Tomography Segmentation for Craniomaxillofacial Surgery Using a Real Surgical Database.Plastic and reconstructive surgery. Global open · 2026Article
- Automatic recognition and measurement of anatomical structures associated with the elevation of the maxillary sinus floor by deep learning on cone-beam computed tomographic scans.BMC oral health · 2026Article
- Segmenting beyond the imaging data: creation of anatomically valid edentulous mandibular geometries for surgical planning using artificial intelligence.Clinical oral investigations · 2025Article
- Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry.Orthodontics & craniofacial research · 2025Article
- Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment.Physics and imaging in radiation oncology · 2025Article
- Image-based Mandibular and Maxillary Parcellation and Annotation using Computer Tomography (IMPACT): A Deep Learning-based Clinical Tool for Orodental Dose Estimation and Osteoradionecrosis Assessment.medRxiv : the preprint server for health sciences · 2025Article
- Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.Dento maxillo facial radiology · 2025Article
- Design and evaluation of a deep learning-based automatic segmentation of maxillary and mandibular substructures using a 3D U-Net.Clinical and translational radiation oncology · 2024Article
- Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.Dento maxillo facial radiology · 2024Article
- Semi or fully automatic tooth segmentation in CBCT images: a review.PeerJ. Computer science · 2024Article
- Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network.International journal of computer assisted radiology and surgery · 2023Article
- Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning.Tomography (Ann Arbor, Mich.) · 2023Article
- Advantages of a Training Course for Surgical Planning in Virtual Reality for Oral and Maxillofacial Surgery: Crossover Study.JMIR serious games · 2023Article
- Three-Dimensional Innovations in Personalized Surgery.Journal of personalized medicine · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Establishing a Point-of-Care Virtual Planning and 3D Printing Program.Seminars in plastic surgery · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications.
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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.