Unsupervised Pre-Training Improves Tooth Segmentation in 3-Dimensional Intraoral Mesh Scans

Abstract

Accurate tooth segmentation in 3-Dimensional (3D) intraoral scanned (IOS) mesh data is an essential step for many practical dental applications. Recent research highlights the success of deep learning based methods for end-to-end 3D tooth segmentation, yet most of them are only trained or validated with a small dataset as annotating 3D IOS dental surfaces requires complex pipelines and intensive human efforts. In this paper, we propose a novel method to boost the performance of 3D tooth segmentation leveraging large-scale unlabeled IOS data. Our tooth segmentation network is first pre-trained with an unsupervised learning framework and point-wise contrastive learning loss on the large-scale unlabeled dataset and subsequently fine-tuned on a small labeled dataset. With the same amount of annotated samples, our method can achieve a mIoU of 89.38%, significantly outperforming the supervised counterpart. Moreover, our method can achieve better performance with only 40% of the annotated samples as compared to the fully supervised baselines. To the best of our knowledge, we present the first attempt of unsupervised pretraining for 3D tooth segmentation, demonstrating its strong potential in reducing human efforts for annotation and verification.