Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans

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dc.contributor.authorUnal, Turkan
dc.contributor.authorKuran, Alican
dc.contributor.authorGulsen, Ibrahim Tevfik
dc.contributor.authorKizilay, Fatma Nur
dc.contributor.authorGulsen, Emine
dc.contributor.authorOzudogru, Semanur
dc.contributor.authorGordeli, Kadir
dc.date.accessioned2026-01-24T12:29:10Z
dc.date.available2026-01-24T12:29:10Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractBackgroundImpacted canines are one of the most frequently encountered dental anomalies in maxillofacial practice. Accurate localization of these teeth is crucial for treatment planning, and Cone Beam Computed Tomography (CBCT) offers detailed 3D imaging for this purpose. However, manual segmentation on CBCT scans is time-consuming and subject to inter-observer variability. This study aimed to develop a deep learning model based on nnU-Net v2 for the automatic segmentation of impacted canines and to evaluate its performance using both classification and segmentation metrics.MethodsA total of 159 CBCT scans containing impacted canines were retrospectively collected and annotated using web-based segmentation software. Model training was performed using the nnU-Net v2 architecture with a learning rate of 0.00001 for 1000 epochs. The performance of the model was evaluated using recall and precision. In addition, segmentation performance was assessed using Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (95% HD in mm), and Intersection over Union (IoU).ResultsThe nnU-Net v2 model achieved high performance in the detection and segmentation of impacted canines. The values obtained for recall and precision 0.90 and 0.82, respectively. The segmentation metrics were also favorable, with a DSC of 0.84, 95% HD of 7.07 mm, and IoU of 0.74, indicating good overlap between predicted and reference segmentations.ConclusionsThe results suggest that the nnU-Net v2-based deep learning model can effectively and autonomously segment impacted canines in CBCT volumes. Its strong performance highlights the potential of artificial intelligence to improve diagnostic efficiency in dentomaxillofacial radiology.
dc.identifier.doi10.1186/s12903-025-07117-5
dc.identifier.issn1472-6831
dc.identifier.issue1
dc.identifier.pmid41437345
dc.identifier.scopus2-s2.0-105025641157
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1186/s12903-025-07117-5
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5166
dc.identifier.volume25
dc.identifier.wosWOS:001645394800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBmc
dc.relation.ispartofBmc Oral Health
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectArtificial intelligence
dc.subjectCBCT
dc.subjectDeep learning
dc.subjectImpacted canine
dc.titleDeep learning-based 3D automatic segmentation of impacted canines in CBCT scans
dc.typeArticle

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