Wang Li, Gao Yaozong, Shi Feng, Li Gang, Chen Ken-Chung, Tang Zhen, Xia James J, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513.
Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030.
Med Phys. 2016 Jan;43(1):336. doi: 10.1118/1.4938267.
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.
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.
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).
The authors have developed and validated a novel fully automated method for CBCT segmentation.
锥形束计算机断层扫描（CBCT）在颅颌面（CMF）畸形患者的诊断和治疗计划中是一种使用越来越频繁的成像方式。CBCT图像的精确分割是为CMF畸形患者生成用于诊断和治疗计划的三维模型的关键步骤。然而，由于束硬化、成像噪声、不均匀性、截断和最大牙尖交错等导致的图像伪影，CBCT的分割具有一定难度。
在本文中，作者提出了一种新的自动分割方法来解决这些问题。具体而言，作者首先采用多数投票法，基于多个对齐的专家分割CBCT图像估计下颌骨和上颌骨的初始分割概率图。这些概率图为CBCT分割提供了重要的先验指导。然后，作者从CBCT中提取外观特征，并从初始概率图中提取上下文特征，以训练能够选择用于分割的判别性特征的第一层随机森林分类器。基于训练好的第一层分类器，更新概率图，并将其用于进一步训练下一层随机森林分类器。通过使用原始CBCT特征和更新后的分割概率图迭代训练后续的随机森林分类器，可以得到一系列用于精确分割CBCT图像的分类器。
基于手动标注的真值对30名受试者的CBCT分割结果进行了定量和定性验证。作者方法得到的下颌骨和上颌骨的平均骰子系数分别为0.94和0.91，显著优于基于稀疏表示的现有方法（p值<0.001）。
作者开发并验证了一种用于CBCT分割的新型全自动方法。
