Title: Semi-supervised segmentation of tooth from 3D Scanned Dental Arches

Dental offices tackle thousands of dental reconstructions every year. Complexity and abnormalities in dentition make segmentation of an optical scan a challenging manual task that takes 45 minutes on average. The present work improves the generalization of currently available deep learning segmentation model on 3D dental arches by introducing a new loss function to leverage unlabeled available data. The semi-supervised segmentation network is trained using a joint loss that combines a supervised loss of annotated input and a self-supervised loss of non-labeled input. Our results showed that combining self-supervised and supervised learning improved the segmentation score by 13 % compared with purely supervised learning for the same amount of labeled data. It is concluded that combining representations obtained from self-supervised learning with supervised learning improves the generalization of the 3D tooth segmentation model in the case of few available labeled data.