Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression

  • Yuru Pei
  • Yungeng Zhang
  • Haifang Qin
  • Gengyu Ma
  • Yuke Guo
  • Tianmin Xu
  • Hongbin Zha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

The 2D-3D registration is a cornerstone to align the inter-treatment X-ray images with the available volumetric images. In this paper, we propose a CNN regression based non-rigid 2D-3D registration method. An iterative refinement scheme is introduced to update the reference volumetric image and the digitally-reconstructed-radiograph (DRR) for convergence to the target X-ray image. The CNN-based regressor represents the mapping between an image pair and the in-between deformation parameters. In particular, the short residual connections in the convolution blocks and long jump connections for the multi-scale feature map fusion facilitate the information propagation in training the regressor. The proposed method has been applied to 2D-3D registration of synthetic X-ray and clinically-captured CBCT images. Experimental results demonstrate the proposed method realizes an accurate and efficient 2D-3D registration of craniofacial images.

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 61272342.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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