title: Automated segmentation of CBCT image using spiral CT atlases and convex optimization.
abstract: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Li
authors: L
authors: Wang
authors: The Methodist Hospital Research Institute, Houston, Texas, USA.
authors: Ken Chung
authors: KC
authors: Chen
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Feng
authors: F
authors: Shi
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Shu
authors: S
authors: Liao
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Gang
authors: G
authors: Li
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Yaozong
authors: Y
authors: Gao
authors: Shanghai Jiao Tong University Ninth Hospital, Shanghai, China.
authors: Steve G F
authors: SG
authors: Shen
authors: Shanghai Jiao Tong University Ninth Hospital, Shanghai, China.
authors: Jin
authors: J
authors: Yan
authors: Hong Kong Dental Implant & Maxillofacial Centre, Hong Kong, China.
authors: Philip K M
authors: PK
authors: Lee
authors: Hong Kong Dental Implant & Maxillofacial Centre, Hong Kong, China.
authors: Ben
authors: B
authors: Chow
authors: Hong Kong Dental Implant & Maxillofacial Centre, Hong Kong, China.
authors: Nancy X
authors: NX
authors: Liu
authors: The Methodist Hospital Research Institute, Houston, Texas, USA.
authors: James J
authors: JJ
authors: Xia
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Dinggang
authors: D
authors: Shen
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D005260:Female; D006801:Humans; D008297:Male; D019767:Maxillofacial Abnormalities / Q000000981:diagnostic imaging*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D054894:Spiral Cone-Beam Computed Tomography / Q000379:methods*; D055815:Young Adult