IEEE Trans Pattern Anal Mach Intell. 2022 Oct;44(10):6562-6568. doi: 10.1109/TPAMI.2021.3086072. Epub 2022 Sep 14.
Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to overcome the computational complexity caused by high-dimensional data and the curse of dimensionality associated with limited training dataset. The obtained 2D panoramic images are then used to identify 2D individual teeth and capture loose- and tight- regions of interest (ROIs) of 3D individual teeth. Finally, accurate 3D individual tooth segmentation is achieved using both loose and tight ROIs. Experimental results showed that the proposed method achieved an F1-score of 93.35 percent for tooth identification and a Dice similarity coefficient of 94.79 percent for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry.
从锥形束计算机断层扫描（CBCT）图像中准确、自动地分割三维（3D）个体牙齿是一个具有挑战性的问题，因为从相邻牙齿及其周围牙槽骨中分离出单个牙齿非常困难。因此，本文提出了一种从牙科 CBCT 图像中自动识别和分割 3D 个体牙齿的方法。该方法通过开发基于深度学习的分层多步模型来解决上述困难。首先，它自动生成上下颌全景图像，以克服高维数据引起的计算复杂性和有限训练数据集带来的维度灾难。然后，使用获得的 2D 全景图像来识别 2D 个体牙齿，并捕获 3D 个体牙齿的松散和紧密感兴趣区域（ROI）。最后，使用松散和紧密 ROI 实现准确的 3D 个体牙齿分割。实验结果表明，所提出的方法在牙齿识别方面的 F1 得分为 93.35%，在个体 3D 牙齿分割方面的骰子相似系数为 94.79%。结果表明，该方法为数字牙科提供了一种有效的临床实用框架。
