Abstract
The recognition of dental pulp calcification has important value for oral clinic, which determines the subsequent treatment decision. However, the recognition of dental pulp calcification is remarkably difficult in clinical practice due to its atypical morphological characteristics. In addition, pulp calcification is also difficult to be visualized in high-resolution CBCT due to its small area and weak contrast. In this work, we proposed a new method of tooth segmentation, identification and pulp calcification recognition based on Transformer to achieve accurate recognition of pulp calcification in high-resolution CBCT images. First, in order to realize that the network can handle extremely high-resolution CBCT, we proposed a coarse-to-fine method to segment the tooth instance in the down-scaled low-resolution CBCT image, and then back to the high-resolution CBCT image to intercept the region of the tooth as the input for the fine segmentation, identification and pulp calcification recognition. Then, in order to enhance the weak distinction between normal teeth and calcified teeth, we proposed tooth instance correlation and triple loss to improve the recognition performance of calcification. Finally, we built a multi-task learning architecture based on Transformer to realize the tooth segmentation, identification and calcification recognition for mutual promotion between tasks. The clinical data verified the effectiveness of the proposed method for the recognition of pulp calcification in high-resolution CBCT for digital dentistry.
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
Yang, Y.M., et al.: CBCT-aided microscopic and ultrasonic treatment for upper or middle thirds calcified root canals. BioMed Res. Int. 1–9, 2016 (2016)
Patel, S., Brown, J., Pimental, T., Kelly, R., Abella, F., Durack, C.: Cone beam computed tomography in endodontics - a review of the literature. Int. Endodont. J. (2019)
Duan, W., Chen, Y., Zhang, Q., Lin, X., Yang, X.: Refined tooth and pulp segmentation using u-net in CBCT image. Dentomaxillofacial Radiol. 20200251 (2021)
Cui, Z., Li, C., Wang, W.: Toothnet: automatic tooth instance segmentation and identification from cone beam CT images. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, 16–20 June 2019, pp. 6368–6377. Computer Vision Foundation/IEEE (2019)
Wu, X., Chen, H., Huang, Y., Guo, H., Qiu, T., Wang, L.: Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam CT. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 939–942 (2020)
Cui, Z., et al.: Hierarchical morphology-guided tooth instance segmentation from CBCT images. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 150–162. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78191-0_12
Cui, Z., et al.: A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat. Commun. 13, 2096 (2022)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Bhattacharjee, D., Zhang, T., Süsstrunk, S., Salzmann, M.: Mult: an end-to-end multitask learning transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12031–12041 (2022)
Xia, L., et al.: 3d vessel-like structure segmentation in medical images by an edge-reinforced network. Med. Image Anal. 82, 102581 (2022)
Shao, Z., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural Inf. Process. Syst. 34, 2136–2147 (2021)
Hou, Z., Yu, B., Tao, D.: Batchformer: learning to explore sample relationships for robust representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7256–7266 (2022)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
Acknowledgement
This research is supported by the school-enterprise cooperation project (No.6401-222-127-001).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, S. et al. (2023). Transformer-Based Tooth Segmentation, Identification and Pulp Calcification Recognition in CBCT. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_68
Download citation
DOI: https://doi.org/10.1007/978-3-031-43904-9_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43903-2
Online ISBN: 978-3-031-43904-9
eBook Packages: Computer ScienceComputer Science (R0)
