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

Authors

  • Wang Li,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599
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  • Chen Ken Chung,

    1. Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403
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  • Gao Yaozong,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599
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  • Shi Feng,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599
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  • Liao Shu,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599
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  • Li Gang,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599
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  • Shen Steve G. F.,

    1. Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth Peopleˈs Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011
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  • Yan Jin,

    1. Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth Peopleˈs Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011
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  • Lee Philip K. M.,

    1. Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077
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  • Chow Ben,

    1. Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077
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  • Liu Nancy X.,

    1. Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050
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  • Xia James J.,

    1. Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065; and Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth Peopleˈs Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011
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  • Shen Dinggang

    Corresponding author
    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea 136701
    • Author to whom correspondence should be addressed. Electronic mail: dgshen@med.unc.edu; Telephone: 919-966-3535; Fax: 919-843-2641.

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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 amaximum 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.

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