title: Automated segmentation of dental CBCT image with prior-guided sequential random forests.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513.
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.affiliation: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030.
authors.forename: Ken-Chung
authors.initials: KC
authors.lastname: Chen
authors.affiliation: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Tang
authors.affiliation: 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.
authors.forename: James J
authors.initials: JJ
authors.lastname: Xia
authors.affiliation: 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.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001331:Automation; D002648:Child; D054893:Cone-Beam Computed Tomography; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008334:Mandible / Q000000981:diagnostic imaging*; D008437:Maxilla / Q000000981:diagnostic imaging*; D008875:Middle Aged; D011336:Probability; D055815:Young Adult