Related Experiment Video
Updated: Sep 17, 2026
Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
Published on: September 8, 2023
CRANIOSEG: Deep Learning-Based Automated Segmentation of Craniomaxillofacial Anatomical Structures on Cone-Beam
İbrahim Şevki Bayrakdar1, Alican Kuran2, Mehmet Uğurlu3
1Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Türkiye.
Objective:
To develop and evaluate an nnU-Net v2-based deep-learning model for fully automated multiclass segmentation of 27 craniomaxillofacial anatomical structures on cone-beam computed tomography (CBCT).
Methods:
This retrospective study included CBCT scans from 106 adult patients. Twenty-seven anatomical structures were manually segmented in 3D Slicer to establish a consensus reference standard using a hierarchical annotation workflow. The dataset was divided at the patient level into training (n = 96) and held-out internal test (n = 10) sets. A three-dimensional full-resolution nnU-Net v2 model was trained and evaluated using the Dice similarity coefficient (DSC), Jaccard index (IoU), precision, recall, the 95th percentile Hausdorff distance (HD95), and the average symmetric surface distance (ASSD).
Results:
Across all 27 anatomical structures, the model achieved a mean DSC of 0.850 ± 0.132, a mean IoU of 0.773 ± 0.159, a mean precision of 0.863, a mean recall of 0.868, and a mean HD95 of 2.34 mm. The highest category-level performance was observed for the paranasal sinuses (DSC 0.961) and the craniofacial osseous framework (DSC 0.961), whereas the skull-base fissures and fossae showed the lowest performance (DSC 0.694), primarily because of the difficulty in segmenting the pterygopalatine fossa (DSC 0.377). Large, well-defined structures, including the mandible (DSC 0.976) and maxillary sinus (DSC 0.977), were segmented with excellent accuracy, whereas smaller, low-contrast structures such as the mandibular canal (DSC 0.654) remained more challenging.
Conclusions:
This study demonstrates the technical feasibility of simultaneously segmenting 27 craniomaxillofacial anatomical structures from CBCT images using a single nnU-Net v2 model on a selected internal dataset. Overall, the model achieved strong segmentation performance, with particularly high overlap values for large, well-defined structures such as the mandible, maxillary sinus, sphenoid sinus, and upper skull. Performance varied among structures, with lower values observed mainly for several small, low-contrast, and morphologically complex structures.
