[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Temporal Consistent 2D-3D Registration of Lateral Cephalograms and Cone-Beam Computed Tomography Images

  • Conference paper
  • First Online:
Machine Learning in Medical Imaging (MLMI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11046))

Included in the following conference series:

Abstract

Craniofacial growths and developments play an important role in treatment planning of orthopedics and orthodontics. Traditional growth studies are mainly on longitudinal growth datasets of 2D lateral cephalometric radiographs (LCR). In this paper, we propose a temporal consistent 2D-3D registration technique enabling 3D growth measurements of craniofacial structures. We initialize the independent 2D-3D registration by the convolutional neural network (CNN)-based regression, which produces the dense displacement field of the cone-beam computed tomography (CBCT) image when given the LCR. The temporal constraints of the growth-stable structures are used to refine the 2D-3D registration. Instead of traditional independent 2D-3D registration, we jointly solve the nonrigid displacement fields of a series of input LCRs captured at different ages. The hierarchical pyramid of the digitally reconstructed radiographs (DRR) is introduced to fasten the convergence. The proposed method has been applied to the growth dataset in clinical orthodontics. The resulted 2D-3D registration is consistent with both the input LCRs concerning the structural contours and the 3D volumetric images regarding the growth-stable structures.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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

Similar content being viewed by others

References

  1. Chen, L., Liu, J., Xu, T., Lin, J.: Longitudinal study of relative growth rates of the maxilla and the mandible according to quantitative cervical vertebral maturation. Am. J. Orthod. Dentofac. Orthop. 137(6), 736.e1–736.e8 (2010)

    Google Scholar 

  2. Markelj, P., Tomaževič, D., Likar, B., Pernuš, F.: A review of 3d/2d registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2012)

    Article  Google Scholar 

  3. Miao, S., Wang, Z.J., Liao, R.: A cnn regression approach for real-time 2d/3d registration. IEEE Trans. Med. Imaging 35(5), 1352–1363 (2016)

    Article  Google Scholar 

  4. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference, pp. 41.1–41.12 (2015)

    Google Scholar 

  5. Pei, Y., Dai, F., Xu, T., Zha, H., Ma, G.: Volumetric reconstruction of craniofacial structures from 2d lateral cephalograms by regression forest. In: IEEE International Conference on Image Processing, pp. 4052–4056 (2016)

    Google Scholar 

  6. Pei, Y., et al.: Non-rigid craniofacial 2D-3D registration using CNN-based regression. In: Cardoso, M.J., Arbel, T., Carneiro, G., Syeda-Mahmood, T., Tavares, J.M.R.S., Moradi, M., Bradley, A., Greenspan, H., Papa, J.P., Madabhushi, A., Nascimento, J.C., Cardoso, J.S., Belagiannis, V., Lu, Z. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 117–125. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_14

  7. Perona, P., Shiota, T., Malik, J.: Anisotropic diffusion. In: Geometry-Driven Diffusion in Computer Vision, pp. 73–92 (1994)

    Google Scholar 

  8. Yu, W., Tannast, M., Zheng, G.: Non-rigid free-form 2d–3d registration using a b-spline-based statistical deformation model. Pattern Recognit. 63, 689–699 (2017)

    Article  Google Scholar 

  9. Yue, W., Yin, D., Li, C., Wang, G., Xu, T.: Automated 2-d cephalometric analysis on x-ray images by a model-based approach. IEEE Trans. Biomed. Eng. 53(8), 1615–1623 (2006)

    Article  Google Scholar 

  10. Zheng, G.: Statistically deformable 2d/3d registration for accurate determination of post-operative cup orientation from single standard x-ray radiograph. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009, pp. 820–827 (2009)

    Google Scholar 

  11. Zheng, G.: Effective incorporating spatial information in a mutual information based 3d–2d registration of a ct volume to x-ray images. Comput. Med. Imaging Graphics 34(7), 553–562 (2010)

    Article  Google Scholar 

  12. Zheng, G.: 3d volumetric intensity reconsturction from 2d x-ray images using partial least squares regression. In: IEEE International Symposium on Biomedical Imaging, pp. 1268–1271 (2013)

    Google Scholar 

  13. Zheng, G., Gollmer, S., Schumann, S., Dong, X., Feilkas, T., Ballester, M.A.G.: A 2d/3d correspondence building method for reconstruction of a patient-specific 3d bone surface model using point distribution models and calibrated x-ray images. Med. Image Anal. 13(6), 883–899 (2009)

    Article  Google Scholar 

  14. Zollei, L., Grimson, E., Norbash, A., Wells, W.: 2d–3d rigid registration of x-ray fluoroscopy and ct images using mutual information and sparsely sampled histogram estimators. In: IEEE Conference on Computer Vision and Pattern Recognition (2001)

    Google Scholar 

Download references

Acknowledgment

This work was supported by NSFC 61272342.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuru Pei .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Y. et al. (2018). Temporal Consistent 2D-3D Registration of Lateral Cephalograms and Cone-Beam Computed Tomography Images. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00919-9_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00918-2

  • Online ISBN: 978-3-030-00919-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics