Automated segmentation of dental CBCT image with prior-guided sequential random forests

Authors

  • Wang Li,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
    Search for more papers by this author
  • Gao Yaozong,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
    Search for more papers by this author
  • Shi Feng,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
    Search for more papers by this author
  • Li Gang,

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
    Search for more papers by this author
  • Chen Ken-Chung,

    1. Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030
    Search for more papers by this author
  • Tang Zhen,

    1. Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030
    Search for more papers by this author
  • Xia James J.,

    1. Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist 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, Shanghai Jiao Tong University School of Medicine, Shanghai Ninth People's Hospital, Shanghai 200011, China
    Search for more papers by this author
    • a)

      Authors to whom correspondence should be addressed. Electronic mail: dgshen@med.unc.edu; Telephone: 919-966-3535; Fax: 919-843-2641 and Electronic mail: JXia@HoustonMethodist.org; Telephone: 713-441-5576; Fax: 713-793-1869.

  • Shen Dinggang

    1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
    Search for more papers by this author
    • a)

      Authors to whom correspondence should be addressed. Electronic mail: dgshen@med.unc.edu; Telephone: 919-966-3535; Fax: 919-843-2641 and Electronic mail: JXia@HoustonMethodist.org; Telephone: 713-441-5576; Fax: 713-793-1869.


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 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT.

Methods:

In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images.

Results:

Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors' method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001).

Conclusions:

The authors have developed and validated a novel fully automated method for CBCT segmentation.

Ancillary