BACKGROUND:Automatic segmentation of individual tooth root is a key technology for the reconstruction of the three-dimensional dental model from Cone Beam Computed Tomography (CBCT) images, which is of great significance for the orthodontic, implant and other dental diagnosis and treatment planning. OBJECTIVES:Currently, tooth root segmentation is mainly done manually because of the similar gray of the tooth root and the alveolar bone from CBCT images. This study aims to explore the automatic tooth root segmentation algorithm of CBCT axial image sequence based on deep learning. METHODS:We proposed a new automatic tooth root segmentation method based on the deep learning U-net with AGs. Since CBCT sequence has a strong correlation between adjacent slices, a Recurrent neural network (RNN) was applied to extract the intra-slice and inter-slice contexts. To develop and test this new method for automatic segmentation of tooth roots using CBCT images, 24 sets of CBCT sequences containing 1160 images and 5 sets of CBCT sequences containing 361 images were used to train and test the network, respectively. RESULTS:Applying to the testing dataset, the segmentation accuracy measured by the intersection over union (IOU), dice similarity coefficient (DICE), average precision rate (APR), average recall rate (ARR), and average symmetrical surface distance (ASSD) are 0.914, 0.955, 95.8% , 95.3% , 0.145 mm, respectively. CONCLUSIONS:The study demonstrates that the new method combining attention U-net with RNN yields the promising results of automatic tooth roots segmentation, which has potential to help improve the segmentation efficiency and accuracy in future clinical practice.

中文翻译：

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使用 U-net 和 RNN 自动对锥形束计算机断层扫描图像序列进行牙根分割

背景：单个牙根的自动分割是锥束计算机断层扫描（CBCT）图像重建牙体三维模型的关键技术，对正畸、种植体等牙科诊疗规划具有重要意义。目的：由于CBCT图像中牙根和牙槽骨的灰度相似，目前牙根分割主要是手动完成的。本研究旨在探索基于深度学习的CBCT轴向图像序列自动牙根分割算法。方法：我们提出了一种基于带AGs的深度学习U-net的自动牙根分割方法。由于CBCT序列在相邻切片之间具有很强的相关性，应用循环神经网络 (RNN) 来提取切片内和切片间上下文。为了开发和测试这种使用 CBCT 图像自动分割牙根的新方法，分别使用包含 1160 张图像的 24 组 CBCT 序列和包含 361 张图像的 5 组 CBCT 序列来训练和测试网络。结果：应用于测试数据集，由交集（IOU）、骰子相似系数（DICE）、平均准确率（APR）、平均召回率（ARR）和平均对称表面距离（ASSD）衡量的分割精度分别为 0.914、0.955、95.8%、95.3%、0.145 毫米。结论：该研究表明，将注意力 U-net 与 RNN 相结合的新方法在自动牙根分割方面取得了有希望的结果，