Advertisement

An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

  • Zhusi Zhong
  • Jie Li
  • Zhenxi Zhang
  • Zhicheng Jiao
  • Xinbo GaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder model for landmark detection, which combines global landmark configuration with local high-resolution feature responses. The proposed framework is based on a 2-stage u-net, regressing the multi-channel heatmaps for landmark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, an Expansive Exploration strategy is applied to improve robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated the proposed framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, the proposed framework achieves state-of-the-art results.

Keywords

Landmark detection Deep learning Heatmap regression Attention mechanism 2D X-ray cephalometric analysis 

Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61671339, 61432014 and 61772402, and in part by National High-Level Talents Special Support Program of China under Grant CS31117200001.

References

  1. 1.
    Ching-Wei, W., et al.: Evaluation and comparison of anatomical landmark detection methods for cephalometric X-ray images: a grand challenge. IEEE TMI 34, 1890–1900 (2015)Google Scholar
  2. 2.
    Wang, C.W., et al.: A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 31, 63 (2016)CrossRefGoogle Scholar
  3. 3.
    Ibragimov, B., et al.: Computerized cephalometry by game theory with shape-and appearance-based landmark refinement. In: ISBI (2015)Google Scholar
  4. 4.
    Lindner, C., et al.: Fully automatic cephalometric evaluation using random forest regression-voting. In: ISBI (2015)Google Scholar
  5. 5.
    Lindner, C., et al.: Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Sci. Rep. 6, 33581 (2016)CrossRefGoogle Scholar
  6. 6.
    Jiao, Z., et al.: A deep feature based framework for breast masses classification. Neurocomputing 197, 221–231 (2016)CrossRefGoogle Scholar
  7. 7.
    Jiao, Z., et al.: Deep convolutional neural networks for mental load classification based on EEG data. Pattern Recogn. 76, 582–595 (2018)CrossRefGoogle Scholar
  8. 8.
    Hu, Y., et al.: Mammographic mass detection based on saliency with deep features. In: ICIMCS, pp. 292–297. ACM (2016)Google Scholar
  9. 9.
    Yang, D., et al.: Asymmetry Analysis with sparse autoencoder in mammography. In: ICIMCS, pp. 287–291. ACM (2016)Google Scholar
  10. 10.
    Lee, H., et al.: Cephalometric landmark detection in dental x-ray images using convolutional neural networks. In: Medical Imaging 2017: Computer-Aided Diagnosis, p. 101341 W. International Society for Optics and Photonics (2017)Google Scholar
  11. 11.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  12. 12.
    Long, J., et al.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)Google Scholar
  13. 13.
    Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_27CrossRefGoogle Scholar
  14. 14.
    Guan, Q., et al.: Diagnose like a radiologist: attention guided convolutional neural network for thorax disease classification. arXiv preprint arXiv:1801.09927 (2018)
  15. 15.
    Tuysuzoglu, A., Tan, J., Eissa, K., Kiraly, A.P., Diallo, M., Kamen, A.: Deep adversarial context-aware landmark detection for ultrasound imaging. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 151–158. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00937-3_18CrossRefGoogle Scholar
  16. 16.
    Lin, T.-Y., et al.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhusi Zhong
    • 1
  • Jie Li
    • 1
  • Zhenxi Zhang
    • 1
  • Zhicheng Jiao
    • 2
  • Xinbo Gao
    • 1
    Email author
  1. 1.School of Electronic EngineeringXidian UniversityXi’anChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations