ArticleBMC oral health2023
Accurate mandibular canal segmentation of dental CBCT using a two-stage 3D-UNet based segmentation framework.
Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 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
- Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity.Clinical and experimental dental research · 2026Pooled it
- Automated Multi-class Segmentation of Mandibular Odontogenic Cysts and the Mandibular Canal on CBCT Images Using a Hybrid Deep Learning Framework.Journal of imaging informatics in medicine · 2026Article
- Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning.International dental journal · 2026Article
- A BMC-Net model for the recognition and segmentation of mandibular canal bifurcation.Oral radiology · 2026Article
- Deep learning for automatic segmentation of the inferior alveolar nerve using a hybrid CNN-transformer framework.Scientific reports · 2026Article
- A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.Scientific reports · 2026Article
- Clinically applicable deep learning model for segmentation of the mandibular bone and inferior alveolar canal in CBCT cross-sectional images.BMC oral health · 2026Article
- Deep learning for automated mandibular canal segmentation in CBCT scans.BMC oral health · 2025Article
- Assessing the spatial relationship between mandibular third molars and the inferior alveolar canal using a deep learning-based approach: a proof-of-concept study.BMC oral health · 2025Article
- AI-powered segmentation of bifid mandibular canals using CBCT.BMC oral health · 2025Article
- AI and Dentistry Feature of the International Dental Journal: A Letter.International dental journal · 2025Article
- Alveolar Bone Segmentation Methods in Assessing the Effectiveness of Periodontal Defect Regeneration Through Machine Learning of CBCT Data: A Systematic Review.International journal of biomedical imaging · 2025Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- [Strategies for prevention and treatment of vascular and nerve injuries in mandibular anterior implant surgery].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2024Review
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7 authors.
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Abstract
objectivesThe objective of this study is to develop a deep learning (DL) model for fast and accurate mandibular canal (MC) segmentation on cone beam computed tomography (CBCT).
methodsA total of 220 CBCT scans from dentate subjects needing oral surgery were used in this study. The segmentation ground truth is annotated and reviewed by two senior dentists. All patients were randomly splitted into a training dataset (n = 132), a validation dataset (n = 44) and a test dataset (n = 44). We proposed a two-stage 3D-UNet based segmentation framework for automated MC segmentation on CBCT. The Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95% HD) were used as the evaluation metrics for the segmentation model.
resultsThe two-stage 3D-UNet model successfully segmented the MC on CBCT images. In the test dataset, the mean DSC was 0.875 ± 0.045 and the mean 95% HD was 0.442 ± 0.379.
conclusionsThis automatic DL method might aid in the detection of MC and assist dental practitioners to set up treatment plans for oral surgery evolved MC.
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