Abstract
Computer-aided systems are widely used in digital dentistry to help human experts for accurate and efficient diagnosis and treatment planning. In this paper, we study the problem of tooth defect segmentation in 3-Dimensional (3D) mesh scans, which is a prerequisite task in many dental applications. Existing models usually perform poorly in this task due to the highly imbalanced characteristic of tooth defects. To tackle this issue, we propose a novel triple-stream graph convolutional network named TripleNet to learn multi-scale geometric features from mesh scans for end-to-end tooth defect segmentation. With predefined geometrical features as inputs and a focal loss for training guidance, we achieve state-of-the-art performance on 3D tooth defect segmentation. Our work exhibits the great potential of artificial intelligence for future digital dentistry.
These authors contributed equally.
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References
Beckett, H.: Preservation and restoration of tooth structure. Br. Dent. J. 198(11), 727–727 (2005)
Boulch, A., Le Saux, B., Audebert, N.: Unstructured point cloud semantic labeling using deep segmentation networks. 3dor@ Eurograph. 3, 1–8 (2017)
Chen, H., Li, H., Hu, B., Ma, K., Sun, Y.: A convolutional neural network for teeth margin detection on 3-dimensional dental meshes. arXiv preprint arXiv:2107.03030 (2021)
Cui, Z., et al.: Tsegnet: an efficient and accurate tooth segmentation network on 3D dental model. Med. Image Anal. 69, 101949 (2021)
Deng, H., et al.: An automatic approach to establish clinically desired final dental occlusion for one-piece maxillary orthognathic surgery. Int. J. Comput. Assist. Radiol. Surg. 15(11), 1763–1773 (2020)
Graham, B., Engelcke, M., Van Der Maaten, L.: 3D semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 9224–9232 (2018)
Guo, M.H., Cai, J.X., Liu, Z.N., Mu, T.J., Martin, R.R., Hu, S.M.: PCT: point cloud transformer. Comput. Vis. Media 7(2), 187–199 (2021)
Hao, J., et al.: Toward clinically applicable 3-dimensional tooth segmentation via deep learning. J. Dent. Res. 101(3), 304–311 (2022)
Huang, J., You, S.: Point cloud labeling using 3D convolutional neural network. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2670–2675. IEEE (2016)
Kumar, Y., Janardan, R., Larson, B., Moon, J.: Improved segmentation of teeth in dental models. Comput.-Aid. Des. Appl. 8(2), 211–224 (2011)
Lang, et al.: DLLNet: an attention-based deep learning method for dental landmark localization on high-resolution 3D digital dental models. In: de Bruinjne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 478–487. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_46
Lawin, F.J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F.S., Felsberg, M.: Deep projective 3D semantic segmentation. In: Felsberg, M., Heyden, A., Krüger, N. (eds.) CAIP 2017. LNCS, vol. 10424, pp. 95–107. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64689-3_8
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: convolution on x-transformed points. Adv. Neural Inf. Process. Syst. 31 (2018)
Lian, C., et al.: Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3D intraoral scanners. IEEE Trans. Med. Imaging 39(7), 2440–2450 (2020)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection (2017). 10.48550/ARXIV.1708.02002, https://arxiv.org/abs/1708.02002
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30 (2017)
Raith, S., et al.: Artificial neural networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Comput. Biol. Med. 80, 65–76 (2017)
Sun, D., et al.: Automatic tooth segmentation and dense correspondence of 3D dental model. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 703–712. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_68
Tchapmi, L., Choy, C., Armeni, I., Gwak, J., Savarese, S.: Segcloud: semantic segmentation of 3D point clouds. In: 2017 International Conference on 3D Vision (3DV), pp. 537–547. IEEE (2017)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. ACM Trans. Graph. (TOG) (2019)
Wei, G., et al.: TANet: towards fully automatic tooth arrangement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 481–497. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58555-6_29
Wu, B., Wan, A., Yue, X., Keutzer, K.: Squeezeseg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D lidar point cloud. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1887–1893. IEEE (2018)
Wu, K., Chen, L., Li, J., Zhou, Y.: Tooth segmentation on dental meshes using morphologic skeleton. Comput. Graph. 38, 199–211 (2014)
Wu, T.H., et al.: Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans. IEEE Trans. Med. Imaging (2022)
Wu, W., Qi, Z., Fuxin, L.: Pointconv: deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9621–9630 (2019)
Xu, M., Ding, R., Zhao, H., Qi, X.: Paconv: position adaptive convolution with dynamic kernel assembling on point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3173–3182 (2021)
Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16259–16268 (2021)
Zou, B.J., Liu, S.J., Liao, S.H., Ding, X., Liang, Y.: Interactive tooth partition of dental mesh base on tooth-target harmonic field. Comput. Biol. Med. 56, 132–144 (2015)
Acknowledgement
This work was supported by the National Science Foundation of China 62106222 and the ZJU-Angelalign R &D Center for Intelligent Healthcare. We thank Dr. Huikai Wu and Dr.Chenxi Liu for the helpful discussions and prior work.
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Chen, H., Ge, Y., Wei, J., Xiong, H., Liu, Z. (2022). Tooth Defect Segmentation in 3D Mesh Scans Using Deep Learning. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_15
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