Abstract
Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zanjani, F.G., et al.: Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth. In: Proceedings of the 2nd International Conference on Medical Imaging with Deep Learning (MIDL), vol. 102, pp. 557–571. PMLR (2019)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Hermosilla, P., Ritschel, T., Vázquez, P.P., Vinacua, À., Ropinski, T.: Monte Carlo convolution for learning on non-uniformly sampled point clouds. In: SIGGRAPH Asia 2018 Technical Papers, p. 235. ACM (2018)
Hou, J., Dai, A., Nießner, M.: 3D-SIS: 3D semantic instance segmentation of RGB-D scans. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4421–4430 (2019)
Le, T., Duan, Y.: PointGrid: a deep network for 3D shape understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 9204–9214 (2018)
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on X-transformed points. In: Advances in Neural Information Processing Systems, pp. 820–830 (2018)
Liu, C., Furukawa, Y.: MASC: multi-scale affinity with sparse convolution for 3D instance segmentation. arXiv preprint arXiv:1902.04478 (2019)
Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3D object detection from RGB-D data. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 918–927 (2018)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of IEEE Computer Vision and Pattern Recognition, vol. 1, no. 2, p. 4 (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Wang, W., Yu, R., Huang, Q., Neumann, U.: SGPN: similarity group proposal network for 3D point cloud instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2569–2578 (2018)
Yi, L., Zhao, W., Wang, H., Sung, M., Guibas, L.: GSPN: generative shape proposal network for 3D instance segmentation in point cloud. arXiv preprint arXiv:1812.03320 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zanjani, F.G. et al. (2019). Mask-MCNet: Instance Segmentation in 3D Point Cloud of Intra-oral Scans. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-32254-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32253-3
Online ISBN: 978-3-030-32254-0
eBook Packages: Computer ScienceComputer Science (R0)
