Advertisement

Semi-automatic Cephalometric Landmark Detection on X-ray Images Using Deep Learning Method

  • Yu Song
  • Xu Qiao
  • Yutaro Iwmoto
  • Yen-Wei ChenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Accurate quantitative cephalometry plays a significant role in both diagnosis and treatment. However, it is tedious work and time consuming to label cephalometric landmarks manually. It also requires professional doctors to implement. Deep learning has been a hot topic in recent years and has achieved great success in many image classification and image regression problems. In this paper, we propose a semi-automatic method for detection of cephalometric landmarks using deep learning. We first roughly extract an ROI region for each landmark manually. Then we utilize Resnet50, which is a state-of-the-art convolutional neural network, to detect the landmark in the ROI region. The network directly output the coordinates of the landmark. The dataset we used in this paper is a public dataset—ISBI 2015 grand challenge in dental x-ray image analysis. Experiments demonstrated that the proposed method achieved better results compared with state-of-the-art methods.

Keywords

Cephalometric landmark X-ray Deep learning Resnet 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under the Grant No. 61603218, and in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 18H03267 and No. 17K00420.

References

  1. 1.
    Kaur, A., Singh, C.: Automatic cephalometric landmark detection using Zernike moments and template matching. SIViP 9(1), 117–132 (2015)CrossRefGoogle Scholar
  2. 2.
    Grau, V., Alcaniz, M., Juan, M.C., Monserrat, C., Knoll, C.: Automatic localization of cephalometric landmarks. J. Biomed. Inform. 34(3), 146–156 (2001)CrossRefGoogle Scholar
  3. 3.
    Ferreira, J.T.L., de Souza Telles, C.: Evaluation of the reliability of computerized profile cephalometric analysis. Braz. Dent. J. 13(3), 201–204 (2002)CrossRefGoogle Scholar
  4. 4.
    Yue, W., Yin, D., Li, C., Wang, G., Tianmin, X.: Automated 2-D cephalometric analysis on X-ray images by a model-based approach. IEEE Trans. Biomed. Eng. 53(8), 1615–1623 (2006)CrossRefGoogle Scholar
  5. 5.
    Wang, C.-W., Huang, C.-T., Hsieh, M.-C., Li, C.-H., Chang, S.-W., Li, W.-C., Vandaele, R., et al.: Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: a grand challenge. IEEE Trans. Med. Imaging 34(9), 1890–1900 (2015)CrossRefGoogle Scholar
  6. 6.
    Wang, C.-W., Huang, C.-T., Lee, J.-H., Li, C.-H., Chang, S.-W., Siao, M.-J., Lai, T.-M., et al.: A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 31, 63–76 (2016)CrossRefGoogle Scholar
  7. 7.
    Lindner, C., Cootes, T.F.: Fully automatic cephalometric evaluation using random forest regression-voting. In: IEEE International Symposium on Biomedical Imaging (2015)Google Scholar
  8. 8.
    Ibragimov, B., Likar, B., Pernus, F., Vrtovec, T.: Computerized cephalometry by game theory with shape-and appearance-based landmark refinement. In: Proceedings of International Symposium on Biomedical imaging (ISBI) (2015)Google Scholar
  9. 9.
    Arik, S.Ö., Ibragimov, B., Xing, L.: Fully automated quantitative cephalometry using convolutional neural networks. J. Med. Imaging 4(1), 014501 (2017)CrossRefGoogle Scholar
  10. 10.
    Ramanan, D., Zhu, X.: Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2879–2886 (2012)Google Scholar
  11. 11.
    Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2930–2940 (2013)CrossRefGoogle Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  14. 14.
    Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)Google Scholar
  15. 15.
    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
  16. 16.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)Google Scholar
  17. 17.
    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

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Graduate School of Information Science and EngineeringRitsumeikan UniversityShigaJapan
  2. 2.Department of Biomedical EngineeringShandong UniversityShandongChina

Personalised recommendations