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

Li Wang, Ken Chung Chen, Feng Shi, Shu Liao, Gang Li, Yaozong Gao, Steve G.F. Shen, Jin Yan, Philip K.M. Lee, Ben Chow, Nancy X. Liu, James J. Xia, Dinggang Shen

研究成果: Conference contribution

19 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
頁面251-258
頁數8
版本PART 3
DOIs
出版狀態Published - 2013
事件16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
持續時間: 2013 九月 222013 九月 26

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼PART 3
8151 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
國家Japan
城市Nagoya
期間13-09-2213-09-26

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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