title: Automatic Dent-landmark detection in 3-D CBCT dental volumes.
abstract: Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed tomography (CBCT) dental data. Specifically, a detector is learned using the random forest with sampled context features. Furthermore, we use spacial prior to build a constrained search space other than use the full three dimensional space. The proposed method has been evaluated on a dataset containing 73 CBCT dental volumes and yields promising results.
authors.affiliation: Center for Data Analytics & Biomedical Informatics, Computer & Information Science Department, Temple University, Philadelphia, PA 19122, USA. erkang.cheng@temple.edu
authors.forename: Erkang
authors.initials: E
authors.lastname: Cheng
authors.forename: Jinwu
authors.initials: J
authors.lastname: Chen
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.forename: Huiyang
authors.initials: H
authors.lastname: Deng
authors.forename: Yi
authors.initials: Y
authors.lastname: Wu
authors.forename: Vasileios
authors.initials: V
authors.lastname: Megalooikonomou
authors.forename: Bryce
authors.initials: B
authors.lastname: Gable
authors.forename: Haibin
authors.initials: H
authors.lastname: Ling
mesh_terms: D000465:Algorithms; D002508:Cephalometry / Q000379:methods; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D009970:Orthodontics / Q000295:instrumentation* / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D013997:Time Factors; D014070:Tooth / Q000033:anatomy & histology