  Accurate tooth volume segmentation is a prerequisite for computer-aided
dental analysis. Deep learning-based tooth segmentation methods have achieved
satisfying performances but require a large quantity of tooth data with ground
truth. The dental data publicly available is limited meaning the existing
methods can not be reproduced, evaluated and applied in clinical practice. In
this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully
annotated volumes and 146 unlabeled volumes. We further evaluate several
state-of-the-art tooth volume segmentation strategies based on fully-supervised
learning, semi-supervised learning and active learning, and define the
performance principles. This work provides a new benchmark for the tooth volume
segmentation task, and the experiment can serve as the baseline for future
AI-based dental imaging research and clinical application development.
