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Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization.

Abstract：

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 a maximum a posteriori probability-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.

journal_name

Med Phys

journal_title

Medical physics

authors

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

doi

10.1118/1.4868455

subject

Has Abstract

pub_date

2014-04-01 00:00:00

pages

043503

issue

eissn

0094-2405

issn

2473-4209

journal_volume

pub_type

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Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization.