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

Automated segmentation of CBCT image using spiral CT atlases and convex optimization.
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DOI:
10.1007/978-3-642-40760-4_32
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发表时间:
2013
期刊:
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子:
--
通讯作者:
Shen, Dinggang
Shen, Dinggang
中科院分区:
其他
文献类型:
--
作者:
Wang, Li;Chen, Ken Chung;Shi, Feng;Liao, Shu;Li, Gang;Gao, Yaozong;Shen, Steve G. F.;Yan, Jin;Lee, Philip K. M.;Chow, Ben;Liu, Nancy X.;Xia, James J.;Shen, Dinggang

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锥形束计算机断层扫描(CBCT)是一种越来越多地用于颅颌面(CMF)畸形患者诊断和治疗计划的成像模式。与传统螺旋CT扫描相比,CBCT扫描具有相对较低的成本和较低的辐射剂量。然而,CBCT扫描的一个主要局限性是广泛存在的图像伪影,如噪声、射束硬化和不均匀性,这给从软组织中准确分割骨性结构以及分离下颌骨和上颌骨造成了很大困难。提出了一种新的CBCT图像自动分割方法。在该方法中,我们首先使用稀疏标签融合策略从预定义的螺旋CT图谱估计患者特异性图谱。然后将该患者特异性图谱集成到基于最大后验概率的凸分割框架中,以进行准确分割。最后,通过与手动地面实况分割的比较来验证我们方法的性能。
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.
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