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International Workshop on Machine Learning in Medical Imaging

MLMI 2015: Machine Learning in Medical Imaging pp 102–109Cite as

Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9352))

Abstract

This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the SDM is first constructed from a set of training data using a non-rigid registration algorithm based on b-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D-3D registration algorithm is then presented to iteratively fit the 3D b-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.

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Author information

Authors and Affiliations

  1. Institute for Surgical Technology and Biomechanics, University of Bern, 3014, Bern, Switzerland

    Guoyan Zheng & Weimin Yu

Authors
  1. Guoyan Zheng
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  2. Weimin Yu
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Corresponding author

Correspondence to Guoyan Zheng .

Editor information

Editors and Affiliations

  1. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Luping Zhou

  2. Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA

    Li Wang

  3. Biomedical Engineering, Shanghai Jiaotong University, Shanghai, China

    Qian Wang

  4. Computer Science, Nanjing University, Nanjing, China

    Yinghuan Shi

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Zheng, G., Yu, W. (2015). Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-24888-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24887-5

  • Online ISBN: 978-3-319-24888-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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