School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Phys Med Biol. 2018 Dec 21;64(1):015012. doi: 10.1088/1361-6560/aaf441.
In orthodontic diagnosis and oral treatment planning, 3D tooth model constructed by dental computed tomography (CT) images is an essential and useful assisted tool. In virtue of the higher spatial resolution and lower radiation of x-ray, cone beam computed tomography (CBCT) has been widely used in dental application. However, due to lower signal to noise ratio, vague and weak edge between tooth root and sockets as well as intensity inhomogeneity, the tooth root is easy to be under-segmented and appears false boundary. This paper presents a new hybrid active contour model in a variational level set formulation to segment the tooth root accurately. Initial shape and intensity information from the upper layer is used for next layer's enhancement and shape constraint. The hybrid level set model is constituted by multi-scale local likelihood image fitting (LLIF) energy term, prior shape constraint energy term with adaptive weight and reaction-diffusion (RD) regularization energy term. For detailed interpretation of this hybrid energy model, the intensity information in a narrowband region outside the contour was used to enhance the contrast between tooth dentine and sockets. The LLIF energy term was incorporated into the level set function to overcome the edge fuzziness and intensity inhomogeneity. The shape prior energy term with adaptive weight was used to differentiate the constraint of the contour evolution inside and outside the level set function to improve the capability of curve topology changes. The RD energy term was introduced to effectively regularize the level set evolution. A new measurement for tooth segmentation evaluation was proposed for quantitative validation. The experimental result of the proposed method was compared with two other typical approaches, and was demonstrated to achieve a higher segmentation accuracy.
在口腔正畸诊断和治疗计划中，利用牙科 CT 图像构建的三维牙齿模型是一种必不可少且非常有用的辅助工具。由于 X 射线的空间分辨率更高，辐射量更低，锥形束 CT（CBCT）已广泛应用于牙科领域。然而，由于较低的信噪比、牙齿根部与牙槽骨之间边界模糊和强度不均匀，牙齿根部容易出现欠分割和伪边界。本文提出了一种新的混合主动轮廓模型，用于在变分水平集公式中准确分割牙齿根部。上一层的初始形状和强度信息用于增强和约束下一层的形状。混合水平集模型由多尺度局部似然图像拟合（LLIF）能量项、具有自适应权重的先验形状约束能量项和反应扩散（RD）正则化能量项组成。为了详细解释这种混合能量模型，在轮廓外的窄带区域内使用强度信息来增强牙本质和牙槽骨之间的对比度。将 LLIF 能量项合并到水平集函数中，以克服边缘模糊和强度不均匀。采用具有自适应权重的先验形状约束能量项，区分水平集函数内外的轮廓演化约束，提高曲线拓扑变化的能力。引入 RD 能量项，有效地正则化水平集的演化。提出了一种新的牙齿分割评估测量方法，用于定量验证。将所提出方法的实验结果与另外两种典型方法进行了比较，结果表明其具有更高的分割精度。
