Automated segmentation of dental CBCT image with prior-guided sequential random forests: Automated segmentation of dental CBCT image
Feng Shi, Zhen Tang, Li Wang, Yaozong Gao, Dinggang G. Shen, Gang Li, Ken-Chung Chen, James J. Xia · Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2020
Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT.