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

ABSTRACT:

Background

Impacted 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.

Methods

A 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 archite

SUBMITTER: Unal T 

PROVIDER: S-EPMC12723826 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature