Related Experiment Video
Updated: Jun 26, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
Automatic extraction of mandibular bone geometry for anatomy-based synthetization of radiographs
Kari Antila1, Mikko Lilja, Martti Kalke
1Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, USA. kari.antila@vtt.fi
Abstract:
We present an automatic method for segmenting Cone-Beam Computerized Tomography (CBCT) volumes and synthetizing orthopantomographic, anatomically aligned views of the mandibular bone. The model-based segmentation method was developed having the characteristics of dental CBCT, severe metal artefacts, relatively high noise and high variability of the mandibular bone shape, in mind. First, we applied the segmentation method to delineate the bone. Second, we aligned a model resembling the geometry of orthopantomographic imaging according to the segmented surface. Third, we estimated the tooth orientations based on the local shape of the segmented surface. These results were used in determining the geometry of the synthetized radiograph. Segmentation was done with excellent results: with 14 samples we reached 0.57+/-0.16 mm mean distance from hand drawn reference. The estimation of tooth orientations was accurate with error of 0.65+/-8.0 degrees. An example of these results used in synthetizing panoramic radiographs is presented.

