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Automated Segmentation of CBCT Image Using Spiral CT Atlases and Convex Optimization
Li Wang1, Ken Chung Chen2, Feng Shi1, Shu Liao1, Gang Li1, Yaozong Gao1, Steve GF Shen3, Jin Yan3, Philip K.M. Lee4, Ben Chow4, Nancy X. Liu5, James J. Xia2, and Dinggang Shen1
1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
2The Methodist Hospital Research Institute, Houston, Texas, United States
3Shanghai Jiao Tong University Ninth Hospital, Shanghai, China
4Hong Kong Dental Implant & Maxillofacial Centre, Hong Kong, China
5Peking University School and Hospital of Stomatology, Beijing, China
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.
LNCS 8151, p. 251 ff.
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© Springer-Verlag Berlin Heidelberg 2013