Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes

Cone-beam computed tomography (CBCT) is commonly used for 3-dimensional (3D) evaluation and treatment planning of patients in orthodontics, where precision and reproducibility of landmark annotation are required. Manual landmarking is a time- and effort-consuming task regardless of the practitioner's experience. We introduce a hybrid algorithm for automatic cephalometric landmark annotation on CBCT volumes.

This algorithm is based on a 2-dimensional holistic search using active shape models in coronal and sagittal related projections followed by a 3D knowledge-based searching algorithm on subvolumes for local landmark adjustment. Eighteen landmarks were located on 24 CBCT head scans from a public dataset.

A 2.51-mm mean localization error (SD, 1.60 mm) was achieved when comparing automatic annotations with ground truth.

The proposed hybrid algorithm shows that a fast initial 2-dimensional landmark search can be useful for a more accurate 3D annotation and could save computational time compared with a full-volume analysis. Furthermore, this study shows that full bone structures from CBCT are manageable in a personal computer for 3D modern cephalometry.