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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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Three different methods of tooth segmentation. The automatic tooth segmentation process has three steps in common: (1) orientation, (2) setting of the mesiodistal (MD) points, and (3) segmentation, but the details differ depending on the software’s algorithm. (a) Landmark-based segmentation (LS) using OrthoAnalyzer software: precise MD point setting is required prior to tooth segmentation, and the tooth number and axis are determined. (b) Tooth designation and segmentation (DS) using Autolign software: approximate MD point setting is required, and the tooth number is designated. (c) Automatic tooth segmentation (AS) using LaonSetup software: fully automatic segmentation based on deep learning does not require MD point setting, and manual intervention is not required.