Skip to main content
SpringerLink
Log in

Position-Aware Tooth Segmentation and Numbering with Prior Knowledge Injected

  • Conference paper
  • First Online:
Cross-Cultural Design (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14024))

Included in the following conference series:

  • 687 Accesses

Abstract

Tooth segmentation and numbering are the most fundamental tasks in oral analysis as they are the prerequisite for many popular oral businesses such as root canal therapy and whitening. Despite the growing attention in this research field, the results are still far from satisfactory. Existing methods either employ a complicated multi-stage framework or couple tooth segmentation and numbering together simply by using multi-class segmentation, which is neither convenient to use nor accurate enough. To this end, we propose a single-stage multi-task framework to perform tooth segmentation and numbering in an end-to-end and decoupled fashion. Furthermore, We also involve the prior knowledge of the oral structure in the network and leverage adversarial learning to further improve the accuracy. Extensive experiments on two real-world datasets demonstrate that our proposed method achieves state-of-the-art performance.

C. Li and J. He—Contribute equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    “Adversarial learning” here refers to the process for the GAN model to discriminate between correct and mismatched pairs.

  2. 2.

    https://en.wikipedia.org/wiki/Universal_Numbering_System#/media/File:Universal_Numbering_System.svg.

References

  1. Dentistry-designation system for teeth and areas of the oral cavity (first revision). J. Pierre Fauchard Acad. 8(2), 71–3 (1994)

    Google Scholar 

  2. Bernstein, R.: Tooth numbering systems. N. Y. J. Dent. 55(4), 159 (1985)

    Google Scholar 

  3. Bilgir, E., et al.: An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs. BMC Med. Imaging 21(1), 124 (2021)

    Article  Google Scholar 

  4. Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: real-time instance segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9157–9166 (2019)

    Google Scholar 

  5. Chu, P., et al.: Using octuplet Siamese network for osteoporosis analysis on dental panoramic radiographs. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2579–2582. IEEE (2018)

    Google Scholar 

  6. Chung, M., et al.: Individual tooth detection and identification from dental panoramic X-ray images via point-wise localization and distance regularization. Artif. Intell. Med. 111, 101996 (2021)

    Google Scholar 

  7. Cui, Z., Li, C., Wang, W.: ToothNet: automatic tooth instance segmentation and identification from cone beam CT images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6368–6377 (2019)

    Google Scholar 

  8. Fan, L., et al.: Embracing single stride 3d object detector with sparse transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8458–8468 (2022)

    Google Scholar 

  9. Ghafoorian, M., Nugteren, C., Baka, N., Booij, O., Hofmann, M.: EL-GAN: embedding loss driven generative adversarial networks for lane detection. In: proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)

    Google Scholar 

  10. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  11. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  12. Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  15. Huang, Z., Huang, L., Gong, Y., Huang, C., Wang, X.: Mask scoring R-CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6409–6418 (2019)

    Google Scholar 

  16. Jader, G., Fontineli, J., Ruiz, M., Abdalla, K., Pithon, M., Oliveira, L.: Deep instance segmentation of teeth in panoramic X-ray images. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 400–407. IEEE (2018)

    Google Scholar 

  17. Jiang, C., Xu, H., Liang, X., Lin, L.: Hybrid knowledge routed modules for large-scale object detection. arXiv preprint arXiv:1810.12681 (2018)

  18. Karimian, N., Salehi, H.S., Mahdian, M., Alnajjar, H., Tadinada, A.: Deep learning classifier with optical coherence tomography images for early dental caries detection. In: Lasers in Dentistry XXIV, vol. 10473, p. 1047304. International Society for Optics and Photonics (2018)

    Google Scholar 

  19. Kim, C., Kim, D., Jeong, H., Yoon, S.J., Youm, S.: Automatic tooth detection and numbering using a combination of a CNN and heuristic algorithm. Appl. Sci. 10(16), 5624 (2020)

    Article  Google Scholar 

  20. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  21. Koch, T.L., Perslev, M., Igel, C., Brandt, S.S.: Accurate segmentation of dental panoramic radiographs with U-nets. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 15–19. IEEE (2019)

    Google Scholar 

  22. Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: Proceedings of the European conference on computer vision (ECCV), pp. 734–750 (2018)

    Google Scholar 

  23. Lee, J.H., Kim, D.H., Jeong, S.N., Choi, S.H.: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 77, 106–111 (2018)

    Article  Google Scholar 

  24. Lee, J.S., Adhikari, S., Liu, L., Jeong, H.G., Kim, H., Yoon, S.J.: Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiol. 48(1), 20170344 (2019)

    Article  Google Scholar 

  25. Li, P., Zhao, H., Liu, P., Cao, F.: RTM3D: real-time monocular 3D detection from object keypoints for autonomous driving. arXiv e-prints arXiv:2001.03343 (2020)

  26. Liang, Y., Han, W., Qiu, L., Wu, C., Shao, Y., Wang, K., He, L.: Exploring forensic dental identification with deep learning. Adv. Neural Inf. Process. Syst. 34 (2021)

    Google Scholar 

  27. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  28. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  29. Liu, R., Lehman, J., Molino, P., Such, F.P., Frank, E., Sergeev, A., Yosinski, J.: An intriguing failing of convolutional neural networks and the coordconv solution. arXiv preprint arXiv:1807.03247 (2018)

  30. Long, J., Shelhamer, E., Darrell, T.: Fully Convolutional networks for semantic segmentation. arXiv e-prints arXiv:1411.4038 (2014)

  31. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  32. Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv preprint arXiv:1611.08408 (2016)

  33. Pinheiro, L., Silva, B., Sobrinho, B., Lima, F., Cury, P., Oliveira, L.: Numbering permanent and deciduous teeth via deep instance segmentation in panoramic x-rays. In: Rittner, L., M.D., E.R.C., Lepore, N., Brieva, J., Linguraru, M.G. (eds.) 17th International Symposium on Medical Information Processing and Analysis, vol. 12088, pp. 95–104. International Society for Optics and Photonics, SPIE (2021). https://doi.org/10.1117/12.2606211

  34. Prados-Privado, M., García Villalón, J., Blázquez Torres, A., Martínez-Martínez, C.H., Ivorra, C.: A convolutional neural network for automatic tooth numbering in panoramic images. Biomed. Res. Int. 2021, 3625386 (2021)

    Article  Google Scholar 

  35. Prados-Privado, M., Villalón, J.G., Martínez-Martínez, C.H., Ivorra, C.: Dental images recognition technology and applications: a literature review. Appl. Sci. 10(8), 2856 (2020)

    Article  Google Scholar 

  36. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  37. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

    Google Scholar 

  38. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  39. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)

    Google Scholar 

  40. Silva, B., Pinheiro, L., Oliveira, L., Pithon, M.: A study on tooth segmentation and numbering using end-to-end deep neural networks. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 164–171. IEEE (2020)

    Google Scholar 

  41. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  42. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)

    Google Scholar 

  43. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483 (2013)

    Google Scholar 

  44. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)

    Google Scholar 

  45. Tuzoff, D.V., et al.: Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiol. 48(4), 20180051 (2019)

    Google Scholar 

  46. Wang, Xinlong, Kong, Tao, Shen, Chunhua, Jiang, Yuning, Li, Lei: SOLO: segmenting objects by locations. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 649–665. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58523-5_38

    Chapter  Google Scholar 

  47. Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724–4732 (2016)

    Google Scholar 

  48. Xiao, X., Ji, C., Mudiyanselage, T.B., Pan, Y.: PK-GCN: prior knowledge assisted image classification using graph convolution networks. arXiv preprint arXiv:2009.11892 (2020)

  49. Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P.: PolarMask: single shot instance segmentation with polar representation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12193–12202 (2020)

    Google Scholar 

  50. Xu, R., Tafazzoli, F., Zhang, L., Rehfeld, T., Krehl, G., Seal, A.: Holistic grid fusion based stop line estimation. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 8400–8407. IEEE (2021)

    Google Scholar 

  51. Xu, R., et al.: Pik-Fix: restoring and colorizing old photo. arXiv preprint arXiv:2205.01902 (2022)

  52. Xu, R., Xiang, H., Xia, X., Han, X., Li, J., Ma, J.: OPV2V: an open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 2583–2589. IEEE (2022)

    Google Scholar 

  53. Zhang, L., et al.: Hierarchical road topology learning for urban map-less driving. arXiv preprint arXiv:2104.00084 (2021)

Download references

Author information

Authors and Affiliations

  1. Alibaba Group, Hangzhou, China

    Changlin Li, Jian He, Gaige Wang, Kuilong Liu & Changyuan Yang

Authors
  1. Changlin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gaige Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kuilong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Changyuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changlin Li .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, C., He, J., Wang, G., Liu, K., Yang, C. (2023). Position-Aware Tooth Segmentation and Numbering with Prior Knowledge Injected. In: Rau, PL.P. (eds) Cross-Cultural Design. HCII 2023. Lecture Notes in Computer Science, vol 14024. Springer, Cham. https://doi.org/10.1007/978-3-031-35946-0_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35946-0_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35945-3

  • Online ISBN: 978-3-031-35946-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation