Automated segmentation of CBCT image using spiral CT atlases and convex optimization.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Wang, L; Chen, KC; Shi, F; Liao, S; Li, G; Gao, Y; Shen, SGF; Yan, J; Lee, PKM; Chow, B; Liu, NX; Xia, JJ; Shen, D

Published Date

  • 2013

Published In

  • Med Image Comput Comput Assist Interv

Volume / Issue

  • 16 / Pt 3

Start / End Page

  • 251 - 258

PubMed ID

  • 24505768

Pubmed Central ID

  • PMC3918683

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-40760-4_32


  • eng

Conference Location

  • Germany