Jesús Montúfar1, Marcelo Romero2, Rogelio J Scougall-Vilchis3. 1. Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, Mexico. 2. Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, Mexico. Electronic address: mromeroh@uaemex.mx. 3. Facultad de Odontología, Universidad Autónoma del Estado de México, Toluca, Mexico.
Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS: A 2.51-mm mean localization error (SD, 1.60 mm) was achieved when comparing automatic annotations with ground truth. CONCLUSIONS: 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.
INTRODUCTION: 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. METHODS: 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. RESULTS: A 2.51-mm mean localization error (SD, 1.60 mm) was achieved when comparing automatic annotations with ground truth. CONCLUSIONS: 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.
Authors: Yashodhan M Bichu; Ismaeel Hansa; Aditi Y Bichu; Pratik Premjani; Carlos Flores-Mir; Nikhilesh R Vaid Journal: Prog Orthod Date: 2021-07-05 Impact factor: 2.750