Fully automated segmentation of upper and lower jaws from3D intra-oral surface scanners

Mathieu Leclercq, Antonio Ruellas, Marcela Gurgel, Marilia Yatabe, Jonas Bianchi, Lucia Cevidanes, Martin Andreas Styner, Beatriz Paniagua, Juan Carlos Prieto

Keywords: Deep-learning, Segmentation, dental crown, Universal label id, intra-oral surface

TL;DR: Intra-oral surface automated crown segmentation

Abstract:

In this paper, we present a deep learning based method for surface segmentation. This
technique consists of acquiring 2D views and extracting features from the surface such as
the normal vectors. The generated images are analyzed with a 2D convolutional neural
network, such as a UNET or UNEt TRansformers (UNETR). We test our method in a
dental application for segmentation of crowns. The neural network is trained for the multiclass segmentation, using image labels as ground truth. The segmentation task achieved an average Dice of 0.97, sensitivity of 0.97 and prediction of 0.97.

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Paper Type: both

Primary Subject Area: Segmentation

Secondary Subject Area: Application: Other

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