Advertisement

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

  • Yungeng Zhang
  • Yuru Pei
  • Haifang Qin
  • Yuke Guo
  • Gengyu Ma
  • Tianmin Xu
  • Hongbin Zha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

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.

Notes

Acknowledgment

This work was supported by NSFC 61272342.

References

  1. 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. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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. 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_14CrossRefGoogle Scholar
  7. 7.
    Perona, P., Shiota, T., Malik, J.: Anisotropic diffusion. In: Geometry-Driven Diffusion in Computer Vision, pp. 73–92 (1994)Google Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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. 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)CrossRefGoogle Scholar
  14. 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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yungeng Zhang
    • 1
  • Yuru Pei
    • 1
  • Haifang Qin
    • 1
  • Yuke Guo
    • 2
  • Gengyu Ma
    • 3
  • Tianmin Xu
    • 4
  • Hongbin Zha
    • 1
  1. 1.Key Laboratory of Machine Perception (MOE), Department of Machine IntelligencePeking UniversityBeijingChina
  2. 2.Luoyang Institute of Science and TechnologyLuoyangChina
  3. 3.uSens Inc.San JoseUSA
  4. 4.School of Stomatology, Peking UniversityBeijingChina

Personalised recommendations