地标
锥束ct
计算机科学
背景(考古学)
矢状面
人工智能
计算机视觉
锥束ct
口腔正畸科
过程(计算)
计算机断层摄影术
医学
放射科
地理
操作系统
考古
作者
Erkang Cheng,Jinwu Chen,Jie Yang,Huiyang Deng,Yi Wu,Vasileios Megalooikonomou,Bryce Gable,Haibin Ling
标识
DOI:10.1109/iembs.2011.6091532
摘要
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.
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