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. 2022 Jun 8;12:9429. doi: 10.1038/s41598-022-13595-2

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Schematic view of the deep learning process. After the original dental model was converted to a point cloud model, the dentition and the gingiva were segmented using the two-class DGCNN model. To increase the accuracy of semantic segmentation, we segmented individual teeth and gingiva using the seventeen-class DGCNN model with the vertices of the point cloud of the gingiva reduced to less than twice that of the tooth group. The gingival vertices were then restored. Since some of the segmentation results using DGCNN showed an unclear teeth margin, curvature-based mesh segmentation was used as post-processing to segment the teeth margin. The images of the digital dental model used in this figure were obtained using MeshLab (ver. 1.3.4 BETA, ISTI-CNR, Italy) and Unity Editor (ver.2020.3.23f1, Unity Technologies, USA) software.