In computer-assisted orthodontics, three-dimensional tooth models are required for many medical treatments. Tooth segmentation from cone-beam computed tomography (CBCT) images is a crucial step in constructing the models. However, CBCT image quality problems such as metal artifacts and blurring caused by shooting equipment and patients' dental conditions make the segmentation difficult. In this paper, we propose ToothSegNet, a new framework which acquaints the segmentation model with generated degraded images during training. ToothSegNet merges the information of high and low quality images from the designed degradation simulation module using channel-wise cross fusion to reduce the semantic gap between encoder and decoder, and also refines the shape of tooth prediction through a structural constraint loss. Experimental results suggest that ToothSegNet produces more precise segmentation and outperforms the state-of-the-art medical image segmentation methods.

翻译：在计算机辅助正畸学中，三维牙齿模型是多种医学治疗所需的基础。从锥束计算机断层扫描（CBCT）图像中分割牙齿是构建这些模型的关键步骤。然而，CBCT图像存在的金属伪影、拍摄设备及患者牙齿状况导致的模糊等质量问题，使得分割任务具有挑战性。本文提出牙分割网络，一种新框架，通过在训练过程中引入生成的退化图像使分割模型适应此类问题。该网络利用设计的退化模拟模块中的通道级交叉融合机制，融合高低质量图像信息以缩小编码器与解码器之间的语义差距，并通过结构约束损失优化牙齿预测形状。实验结果表明，牙分割网络能实现更精确的分割，性能优于当前最先进的医学图像分割方法。