Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization
Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization
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DOI：
10.1118/1.4868455
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发表时间：
2014-04-01
期刊：
影响因子：
3.8
通讯作者：
Shen，Dinggang
中科院分区：
文献类型：
--
作者：
Wang，Li;Chen，Ken Chung;Shen，Dinggang
目的：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）畸形患者的诊断和治疗计划的成像方式。 CBCT图像的精确分割是生成三维（3D）模型以用于CMF畸形患者的诊断和治疗计划的重要步骤。然而，由于图像质量差，包括非常低的信噪比以及广泛存在的图像伪影（例如噪声、光束硬化和不均匀性），对 CBCT 图像进行分割具有挑战性。在本文中，作者提出了一种新的自动分割方法来解决这些问题。 方法：为了分割 CBCT 图像，作者提出了一种全自动 CBCT 分割的新方法，通过使用基于块的稀疏表示来（1）从软组织中分割骨结构，以及（2）进一步将下颌骨与上颌骨分离。具体来说，首先提出区域特定的配准策略，将所有图集扭曲到当前测试对象，然后采用基于稀疏的标签传播策略从所有对齐的图集中估计患者特定的图集。最后，将患者特定图谱集成到最大的基于后验概率的凸分割框架中，以实现精确分割。结果：所提出的方法已在包含 15 个 CBCT 图像的数据集上进行了评估。通过与传统的注册策略和基于人群的图集进行比较，验证了所提出的区域特定注册策略和患者特定图集的有效性。实验结果表明，与其他最先进的分割方法相比，该方法取得了最佳的分割精度。结论：作者利用基于块的稀疏表示和凸优化提出了一种新的CBCT分割方法，可以在基于15名患者的CBCT分割中获得相当准确的分割结果。
Purpose:Cone‐beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three‐dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal‐to‐noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems.Methods:To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch‐based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region‐specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse‐based label propagation strategy is employed to estimate a patient‐specific atlas from all aligned atlases. Finally, the patient‐specific atlas is integrated into amaximum a posterioriprobability‐based convex segmentation framework for accurate segmentation.Results:The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region‐specific registration strategy and patient‐specific atlas has been validated by comparing with the traditional registration strategy and population‐based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state‐of‐the‐art segmentation methods.Conclusions:The authors have proposed a new CBCT segmentation method by using patch‐based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.