Objectives To describe the current state of the art regarding technological advances in full-
automatic tooth segmentation approaches from 3D cone-beam computed tomography …

Background With the advent of deep learning in modern computing there has been
unprecedented progress in image processing and segmentation. Deep learning-based …

Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT
(CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this …

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread
rapidly since December 2019. Real-time reverse transcription polymerase chain reaction …

Automatic and accurate segmentation of dental models is a fundamental task in computer-
aided dentistry. Previous methods can achieve satisfactory segmentation results on normal …

Introduction Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a
labor-intensive task considering the limited contrast resolution and potential disturbance by …

Accurate segmentation of the jaw (ie, mandible and maxilla) and the teeth in cone beam
computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment …

Objective To develop and validate a layered deep learning algorithm which automatically
creates three-dimensional (3D) surface models of the human mandible out of cone-beam …

Objectives Automatic tooth segmentation and classification from cone beam computed
tomography (CBCT) have become an integral component of the digital dental workflows …

Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-
beam computerized tomography (CBCT) images is a challenging problem because of the …