Cephalometric analysis

Cone (formal languages)

Algorithm

Computed tomography

Computer science

Cone beam computed tomography

Coronal plane

Radiology

Artificial intelligence

Sagittal plane

Orthodontics

Medicine

Nuclear medicine

Ground truth

Landmark

Annotation

Cephalometry

Computer vision

American Journal of Orthodontics and Dentofacial Orthopedics

<h3>Introduction</h3> 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. <h3>Methods</h3> 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. <h3>Results</h3> A 2.51-mm mean localization error (SD, 1.60 mm) was achieved when comparing automatic annotations with ground truth. <h3>Conclusions</h3> 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.

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