Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the inter-overlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. Our method demonstrated 5.68% and 30.30% better accuracy in the F1 score and aggregated Jaccard index, respectively, when compared to the best performing state-of-the-art algorithms. The major implication of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.

中文翻译：

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来自锥束CT图像的姿势感知实例分割框架用于牙齿分割。

从锥束计算机断层扫描（CBCT）图像进行单个牙齿分割是在几种应用（例如牙齿重整计划和种植体引导模拟）中解剖学了解正畸结构的基本前提。但是，CBCT图像中存在严重的金属伪影会妨碍每个牙齿的精确分割。在这项研究中，我们提出了一种用于像素级标记的神经网络，以利用对金属人工制品具有鲁棒性的实例分割框架。我们的方法包括三个步骤：1）通过姿态回归进行图像裁剪和重新对齐； 2）健壮的单个牙齿检测；以及3）分割。我们首先通过姿态回归神经网络提取患者的对齐信息，以获取感兴趣的体积（VOI）区域并重新对齐输入图像，这减少了牙齿包围盒之间的重叠区域。然后，使用卷积检测器将单个牙齿区域定位在VOI重新排列的图像中。我们通过在区域提议网络中采用非最大抑制和多类分类指标来提高检测器的精度。最后，我们应用卷积神经网络（CNN）通过将逐像素标注任务转换为距离回归任务来执行单个牙齿分割。金属密集型图像增强还用于对金属伪影进行鲁棒分割。结果表明，我们提出的方法优于其他最新方法，特别是对于带有金属伪影的牙齿。我们的方法显示出F1分数和Jaccard汇总指数的准确性分别提高了5.68％和30.30％，与性能最佳的最新算法相比。提出的方法的主要含义有两个方面：1）引入可感知姿势的VOI重排，然后进行健壮的牙齿检测； 2）金属健壮的CNN框架，用于精确的牙齿分割。