Accurate segmentation of oral surgery-related tissues from cone beam computed tomography (CBCT) images can significantly accelerate treatment planning and improve surgical accuracy. In this paper, we propose a fully automated tissue segmentation system for dental implant surgery. Specifically, we propose an image preprocessing method based on data distribution histograms, which can adaptively process CBCT images with different parameters. Based on this, we use the bone segmentation network to obtain the segmentation results of alveolar bone, teeth, and maxillary sinus. We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks. The tooth segmentation results can obtain the order information of the dentition. The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods. Its average Dice scores on the tooth, alveolar bone, maxillary sinus, and mandibular canal segmentation tasks were 96.5%, 95.4%, 93.6%, and 94.8%, respectively. These results demonstrate that it can accelerate the development of digital dentistry.

从锥形束计算机断层扫描 (CBCT) 图像中准确分割口腔手术相关组织可以显着加快治疗计划并提高手术准确性。在本文中，我们提出了一种用于牙种植手术的全自动组织分割系统。具体来说，我们提出了一种基于数据分布直方图的图像预处理方法，可以自适应地处理具有不同参数的CBCT图像。在此基础上，我们利用骨分割网络获得牙槽骨、牙齿、上颌窦的分割结果。我们使用牙齿和下颌区域作为牙齿分割和下颌神经管分割的ROI区域来实现相应的任务。牙齿分割结果可以获得牙列的顺序信息。相应的实验结果表明，与现有方法相比，我们的方法可以实现更高的分割精度和效率。其在牙齿、牙槽骨、上颌窦和下颌管分割任务上的平均 Dice 得分分别为 96.5%、95.4%、93.6% 和 94.8%。这些结果表明它可以加速数字牙科的发展。