GFACNet: 3D dental segmentation from intraoral scans integrating geometric features and anatomical constraints

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Abstract

Dental segmentation is a critical step in computer-aided orthodontic treatment planning, but accurate segmentation still faces numerous challenges due to complex tooth morphology, ambiguous gingival boundaries, and clinical issues such as malformed teeth, crowding, and malocclusion. This paper proposes GFACNet, a network that integrates geometric features and anatomical constraints for 3D dental segmentation from intraoral scan data. Our method comprises three key innovations: 1) a morphology-aware graph construction (MAGC) mechanism that adaptively constructs graph structures based on dental geometric characteristics, 2) a multi scale transformer (MST) feature integration module that processes features at different scales while capturing both local and global context, and 3) a hierarchical anatomical constraint loss (HACL) that incorporates multi level anatomical features to guide anatomically consistent segmentation. Experiments on real intraoral scanning datasets demonstrate that GFACNet significantly outperforms existing methods in handling complex dental morphologies, particularly in cases of malformed and missing teeth. Additionally, our method requires reduced computational resources while providing a more practical solution for clinical applications.

Keywords

3D dental segmentation / intraoral scanning / geometric features / tooth segmentation

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References

[1] V. Majanga, S. Viriri, A survey of dental caries segmentation and detection techniques, Sci. World J. , 2022 (2022), 8415705. https://doi.org/10.1155/2022/8415705

[2] Z. Cui, C. Li, N. Chen, G. Wei, R. Chen, Y. Zhou, et al., TSegNet: An efficient and accurate tooth segmentation network on 3D dental model, Med. Image Anal., 69 (2020), 101949. https://doi.org/10.1016/j.media.2020.101949

[3] S. Hou, T. Zhou, Y. Liu, P. Dang, H. Lu, H. Shi, Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement, Comput. Biol. Med., 152 (2023), 106296. https://doi.org/10.1016/j.compbiomed.2022.106296

[4] T. J. Jang, K. C. Kim, H. C. Cho, J. K. Seo, A fully automated method for 3D individual tooth identification and segmentation in dental CBCT, IEEE Trans. Pattern Anal. Mach. Intell., 44 (2021), 6562-6568. https://doi.org/10.1109/TPAMI.2021.3086072

[5] T. J. Jang, H. S. Yun, C. M. Hyun, J. Kim, S. Lee, J. K. Seo, Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification, Med. Image Anal., 93 (2024), 103096. https://doi.org/10.1016/j.media.2024.103096

[6] S. Kondo, W. Morita, H. Ohshima, The biological significance of tooth identification based on developmental and evolutional viewpoints, J. Oral Biosci., 64 (2022), 287-302. https://doi.org/10.1016/j.job.2022.05.004

[7] S. Zhuang, G. Wei, Z. Cui, Y. Zhou, Robust hybrid learning for automatic teeth segmentation and labeling on 3D dental models, IEEE Trans. Multimedia, 27 (2025), 792-803. https://doi.org/10.1109/TMM.2023.3289760

[8] T. Ari, H. Sağlam, H. Öksüzoğlu, O. Kazan, İ. Ş. Bayrakdar, S. B. Duman, et al., Automatic feature segmentation in dental periapical radiographs, Diagnostics, 12 (2022), 3081. https://doi.org/10.3390/diagnostics12123081

[9] B. M. Elgarba, S. Van Aelst, A. Swaity, N. Morgan, S. Shujaat, R. Jacobs, Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study, J. Dent., 137 (2023), 104639. https://doi.org/10.1016/j.jdent.2023.104639

[10] B. Y. Tekin, C. Ozcan, A. Pekince, Y. Yasa, An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs, Comput. Biol. Med., 146 (2022), 105547. https://doi.org/10.1016/j.compbiomed.2022.105547

[11] T. Wu, C. Lian, S. Lee, M. Pastewait, C. Piers, J. Liu, et al., Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans, IEEE Trans. Med. Imaging, 41 (2022), 3158-3166. https://doi.org/10.1109/TMI.2022.3180343

[12] Z. Li, T. Liu, J. Wang, C. Zhang, X. Jia, Multi-scale bidirectional enhancement network for 3D dental model segmentation, in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), (2022), 1-5. https://doi.org/10.1109/ISBI52829.2022.9761556

[13] C. R. Qi, H. Su, K. Mo, L. J. Guibas, Pointnet: Deep learning on point sets for 3D classification and segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2017), 652-660.

[14] C. R. Qi, L. Yi, H. Su, L. J. Guibas, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, in Advances in Neural Information Processing Systems, 30 (2017).

[15] C. Lian, L. Wang, T. Wu, F. Wang, P. Yap, C. Ko, et al., Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3D intraoral scanners, IEEE Trans. Med. Imaging, 39 (2020), 2440-2450. https://doi.org/10.1109/TMI.2020.2971730

[16] R. Xie, Y. Yang, Z. Chen, WITS: Weakly-supervised individual tooth segmentation model trained on box-level labels, Pattern Recognit., 133 (2023), 108974. https://doi.org/10.1016/j.patcog.2022.108974

[17] A. Haghanifar, M. M. Majdabadi, S. Haghanifar, Y. Choi, S. Ko, PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier, Multimed. Tools Appl., 82 (2023), 27659-27679. https://doi.org/10.1007/s11042-023-14435-9

[18] Y. Li, R. Bu, M. Sun, W. Wu, X. Di, B. Chen, Pointcnn: Convolution on x-transformed points, in Advances in Neural Information Processing Systems, 31 (2018).

[19] Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, J. M. Solomon, Dynamic graph CNN for learning on point clouds, ACM Trans. Graph., 38 (2019), 1-12. https://doi.org/10.1145/3326362

[20] H. Zhao, L. Jiang, J. Jia, P. H. S. Torr, V. Koltun, Point transformer, in Proceedings of the IEEE/CVF International Conference on Computer Vision , (2021), 16259-16268.

[21] M. Guo, J. Cai, Z. Liu, T. Mu, R. R. Martin, S. Hu, PCT: Point cloud transformer, Comput. Vis. Media, 7 (2021), 187-199. https://doi.org/10.1007/s41095-021-0229-5

[22] X. Ma, C. Qin, H. You, H. Ran, Y. Fu, Rethinking network design and local geometry in point cloud: A simple residual MLP framework, preprint, arXiv:2202.07123.

[23] G. Qian, Y. Li, H. Peng, J. Mai, H. Hammoud, M. Elhoseiny, et al., Pointnext: Revisiting pointnet++ with improved training and scaling strategies, Advances in Neural Information Processing Systems, 35 (2022), 23192-23204.

[24] Y. Ma, Z. Li, Computer aided orthodontics treatment by virtual segmentation and adjustment, in 2010 International Conference on Image Analysis and Signal Processing , (2010), 336-339. https://doi.org/10.1109/IASP.2010.5476100

[25] Y. Kumar, R. Janardan, B. Larson, J. Moon, Improved segmentation of teeth in dental models, Comput. Aided Des. Appl., 8 (2011), 211-224. https://doi.org/10.3722/cadaps.2011.211-224

[26] N. Wongwaen, C. Sinthanayothin, Computerized algorithm for 3D teeth segmentation, Proc. Int. Conf. Electron. Inf. Eng., 1 (2010), 277-280. https://doi.org/10.1109/ICEIE.2010.5559877

[27] B. Graham, M. Engelcke, L. Van Der Maaten, 3D semantic segmentation with submanifold sparse convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), 9224-9232.

[28] J. Zhang, C. Li, Q. Song, L. Gao, Y. Lai, Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space, Graph. Models, 109 (2020), 101071. https://doi.org/10.1016/j.gmod.2020.101071

[29] Z. Lin, Z. He, X. Wang, B. Zhang, C. Liu, W. Su, et al., DBGANet: Dual-branch geometric attention network for accurate 3D tooth segmentation, IEEE Trans. Circuits Syst. Video Technol., 34 (2024), 4285-4298. https://doi.org/10.1109/TCSVT.2023.3331589

[30] A. Rekik, A. Ben-Hamadou, O. Smaoui, F. Bouzguenda, S. Pujades, E. Boyer, TSegLab: Multi-stage 3D dental scan segmentation and labeling, Comput. Biol. Med., 185 (2025), 109535. https://doi.org/10.1016/j.compbiomed.2024.109535

[31] P. Li, C. Gao, F. Liu, D. Meng, Y. Yan, THISNet: Tooth instance segmentation on 3D dental models via highlighting tooth regions, IEEE Trans. Circuits Syst. Video Technol., 34 (2023), 5229-5241. https://doi.org/10.1109/TCSVT.2023.3341805

[32] B. Zou, S. Wang, H. Liu, G. Sun, Y. Wang, F. Zuo, et al., Teeth-SEG: An efficient instance segmentation framework for orthodontic treatment based on multi-scale aggregation and anthropic prior knowledge, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2024), 11601-11610.

[33] X. Chen, H. Tao, H. Zhou, P. Zhou, Y. Deng, Hierarchical and progressive learning with key point sensitive loss for sonar image classification, Multimedia Syst., 30 (2024), 380. https://doi.org/10.1007/s00530-024-01590-8

[34] Y. Apedo, H. Tao, A weakly supervised pavement crack segmentation based on adversarial learning and transformers, Multimedia Syst., 31 (2025), 380. https://doi.org/10.1007/s00530-025-01850-1

[35] F. Naz, M. A. Anwar, M. Qasim, S. S. Bavabeedu, S. U. Khateeb, A. A. Khalil, et al., Clinical evaluation of composite restorations using the Fédération Dentaire Internationale (FDI) criteria: A retrospective cross-sectional study at University Dental Hospital, The University of Lahore, Pakistan, Cureus, 17 (2025), e83582. https://doi.org/10.7759/cureus.83582

[36] S. Liu, J. Chi, C. Wu, F. Xu, X. Yu, SGT-Net: A transformer-based stratified graph convolutional network for 3D point cloud semantic segmentation, Comput. Mater. Continua, 79 (2024). https://doi.org/10.32604/cmc.2024.049450

[37] L. Qiu, C. Ye, P. Chen, Y. Liu, X. Han, S. Cui, Darch: Dental arch prior-assisted 3D tooth instance segmentation with weak annotations, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2022), 20752-20761.

[38] Y. Zhao, Y. Liu, Y. Tan, L. Zhang, T. Huang, C. Gao, 3D dental model segmentation with graph attentional convolution network, Pattern Recognit. Lett., 152 (2021), 79-85. https://doi.org/10.1016/j.patrec.2021.09.005

[39] M. Fey, J. E. Lenssen, F. Weichert, H. Müller, SplineCNN: Fast geometric deep learning with continuous B-spline kernels, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 869-877.

[40] Y. Kumar, R. Janardan, B. Larson, J. Moon, Improved segmentation of teeth in dental models, Comput. Aided Des. Appl., 8 (2011), 211-224. https://doi.org/10.3722/cadaps.2011.211-224