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Automated segmentation of dental CBCT image with prior‐guided sequential random forests
Author(s) -
Wang Li,
Gao Yaozong,
Shi Feng,
Li Gang,
Chen KenChung,
Tang Zhen,
Xia James J.,
Shen Dinggang
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4938267
Subject(s) - segmentation , random forest , artificial intelligence , computer science , ground truth , cone beam computed tomography , image segmentation , classifier (uml) , pattern recognition (psychology) , computer vision , discriminative model , computed tomography , medicine , radiology
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.