Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Zhang, Jianda, Li, Chunpeng, Song, Qiang, Gao, Lin and Lai, Yu-kun ORCID: https://orcid.org/0000-0002-2094-5680 2020. Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space. Graphical Models 109 , 101071. 10.1016/j.gmod.2020.101071

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Abstract

Automatic segmentation of 3D tooth models into individual teeth is an important step in orthodontic CAD systems. 3D tooth segmentation is a mesh instance segmentation task. Complex geometric features on the surface of 3D tooth models often lead to failure of tooth boundary detection, so it is difficult to achieve automatic and accurate segmentation by traditional mesh segmentation methods. We propose a novel solution to address this problem. We map a 3D tooth model isomorphically to a 2D harmonic parameter space and convert it into an image. This allows us to use a CNN to learn a highly robust image segmentation model to achieve automated and accurate segmentation of 3D tooth models. Finally, we map the image segmentation mask back to the 3D tooth model and refine the segmentation result using an improved Fuzzy Clustering-and-Cuts algorithm. Our method has been incorporated into an orthodontic CAD system, and performs well in practice.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	1524-0703
Funders:	The Royal Society
Date of First Compliant Deposit:	2 May 2020
Date of Acceptance:	21 April 2020
Last Modified:	25 Nov 2024 06:45
URI:	https://orca.cardiff.ac.uk/id/eprint/131423

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