  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.
