Chung Minyoung, Lee Minkyung, Hong Jioh, Park Sanguk, Lee Jusang, Lee Jingyu, Yang Il-Hyung, Lee Jeongjin, Shin Yeong-Gil
Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
Department of Orthodontics, Seoul National University School of Dentistry, 101 Daehak-Ro Jongro-Gu, Seoul, 03080, South Korea.
Comput Biol Med. 2020 May;120:103720. doi: 10.1016/j.compbiomed.2020.103720. Epub 2020 Mar 28.
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.
从锥束计算机断层扫描（CBCT）图像中进行单颗牙齿分割是在多种应用中对正畸结构进行解剖学理解的重要前提，例如牙齿重塑规划和种植导板模拟。然而，CBCT图像中严重金属伪影的存在阻碍了每颗单颗牙齿的准确分割。在本研究中，我们提出了一种用于逐像素标注的神经网络，以利用对金属伪影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）对金属具有鲁棒性的单颗牙齿检测，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，以获得感兴趣体积（VOI）区域并重新对齐输入图像，这减少了牙齿边界框之间的相互重叠区域。然后，使用卷积检测器在VOI重新对齐的图像中定位单颗牙齿区域。我们通过在区域提议网络中采用非极大值抑制和多类分类指标来提高检测器的准确性。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进行单颗牙齿分割。还采用了金属增强图像增强技术来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现有方法，特别是对于带有金属伪影的牙齿。与性能最佳的现有算法相比，我们的方法在F1分数和聚合杰卡德指数方面的准确率分别提高了5.68%和30.30%。所提出方法的主要意义有两个方面：1）引入姿态感知的VOI重新对齐，随后进行鲁棒的牙齿检测；2）用于准确牙齿分割的对金属具有鲁棒性的CNN框架。
