3D tooth segmentation is a prerequisite for computer-aided dental diagnosis and treatment. However, segmenting all tooth regions manually is subjective and time-consuming. Recently, deep learning-based segmentation methods produce convincing results and reduce manual annotation efforts, but it requires a large quantity of ground truth for training. To our knowledge, there are few tooth data available for the 3D segmentation study. In this paper, we establish a fully annotated cone beam computed tomography dataset CTooth with tooth gold standard. This dataset contains 22 volumes (7363 slices) with fine tooth labels annotated by experienced radiographic interpreters. To ensure a relative even data sampling distribution, data variance is included in the CTooth including missing teeth and dental restoration. Several state-of-the-art segmentation methods are evaluated on this dataset. Afterwards, we further summarise and apply a series of 3D attention-based Unet variants for segmenting tooth volumes. This work provides a new benchmark for the tooth volume segmentation task. Experimental evidence proves that attention modules of the 3D UNet structure boost responses in tooth areas and inhibit the influence of background and noise. The best performance is achieved by 3D Unet with SKNet attention module, of 88.04 \% Dice and 78.71 \% IOU, respectively. The attention-based Unet framework outperforms other state-of-the-art methods on the CTooth dataset. The codebase and dataset are released.
翻译:3D牙分解是计算机辅助牙科诊断和治疗的一个先决条件。 但是, 对所有牙区进行人工分解是主观和耗时的。 最近, 深学习分解方法产生令人信服的结果, 并减少人工注解的努力, 但需要大量的实地真相来进行培训。 据我们所知, 3D分解研究的牙齿数据很少。 本文中, 我们建立一个带有牙金标准的完全注解的锥形针形计算透析数据集CTooth。 这个数据集包含22卷( 7363片), 并配有有经验的放射翻译加注的优美牙标签。 为确保相对均衡的数据抽样分布, 数据差异包含在Ctooth中, 包括缺失的牙齿和牙科修复。 对这一数据集进行了一些最先进的分解方法。 之后, 我们进一步总结并应用一系列基于3D的内径的内径图变异变量来进行分解。 这项工作为牙齿分解任务提供了一个新的基准。 实验证据证明, 3DUT结构对牙区进行精度分析, 3UNet结构的反推导反应模块, 在牙区进行相对来说, 78 Dnet 模 模型中, 也抑制了Slax