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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 pre-training for 3D tooth segmentation, demonstrating its strong potential in reducing human efforts for annotation and verification.
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Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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