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Rigid Slice-To-Volume Medical Image Registration Through Markov Random Fields

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Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging (BAMBI 2016, MCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10081))

Abstract

Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction. It is usually formulated as an optimization problem and solved using standard continuous methods. In this paper, we discuss how this task be formulated as a discrete labeling problem on a graph. Inspired by previous works on discrete estimation of linear transformations using Markov Random Fields (MRFs), we model it using a pairwise MRF, where the nodes are associated to the rigid parameters, and the edges encode the relation between the variables. We compare the performance of the proposed method to a continuous formulation optimized using simplex, and we discuss how it can be used to further improve the accuracy of our approach. Promising results are obtained using a monomodal dataset composed of magnetic resonance images (MRI) of a beating heart.

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Notes

  1. 1.

    The source code can be downloaded from https://gitlab.com/franco.stramana1/slice-to-volume.

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Author information

Authors and Affiliations

  1. UNICEN University, Tandil, Argentina

    Roque Porchetto & Franco Stramana

  2. CVN, CentraleSupelec-INRIA, Universite Paris-Saclay, Châtenay-Malabry, France

    Nikos Paragios & Enzo Ferrante

Authors
  1. Roque Porchetto
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  2. Franco Stramana
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  3. Nikos Paragios
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  4. Enzo Ferrante
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Corresponding author

Correspondence to Enzo Ferrante .

Editor information

Editors and Affiliations

  1. HES-SO, Sierre, Switzerland

    Henning Müller

  2. Siemens AG, Munich, Germany

    B. Michael Kelm

  3. McGill University, Montreal, Québec, Canada

    Tal Arbel

  4. University of Sydney, Sydney, New South Wales, Australia

    Weidong Cai

  5. University College London, London, United Kingdom

    M. Jorge Cardoso

  6. Medical University of Vienna, Vienna, Austria

    Georg Langs

  7. Technische Universität München, Garching, Germany

    Bjoern Menze

  8. Rutgers The State University of New Jersey, Piscataway, New Jersey, USA

    Dimitris Metaxas

  9. University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Albert Montillo

  10. Brigham and Women’s Hospital, Boston, Massachusetts, USA

    William M. Wells III

  11. University of North Carolina at Charlotte, Charlotte, North Carolina, USA

    Shaoting Zhang

  12. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Albert C.S. Chung

  13. University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom

    Mark Jenkinson

  14. IcoMetrix, Leuven, Belgium

    Annemie Ribbens

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Porchetto, R., Stramana, F., Paragios, N., Ferrante, E. (2017). Rigid Slice-To-Volume Medical Image Registration Through Markov Random Fields. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_16

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  • DOI: https://doi.org/10.1007/978-3-319-61188-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61187-7

  • Online ISBN: 978-3-319-61188-4

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