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Nonuniformly Spaced Control Points Based on Variational Cardiac Image Registration

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14229))

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Abstract

Non-uniformly spaced control points located on the interface of different objects are beneficial for constructing an accurate displacement field for image registration. However, extracting features of non-uniformly spaced control points in images is challenging for convolutional neural networks (CNNs). We extend a probabilistic image registration model using uniformed-spaced control points by employing non-uniformly-spaced control points. We construct a network to extract the image and spatial features of non-uniformly-spaced control points. Moreover, a variational Bayesian (VB) model using a factorized prior is employed to estimate the distribution of latent variables. In theory, we analyze the KL divergence between the posterior and the two separated priors. We found that the factorized prior has the advantage of decreasing the KL divergence, but too more factorized priors, such as the standard normal, might deteriorate registration accuracy. Moreover, we analyze the relationship between the uncertainty of the displacement field and the spatial distribution of control points. Experimental results on four public datasets show that our network outperforms the state-of-arts registration networks and can provide registration uncertainty.

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Acknowledgements

This work is supported by the Shenzhen Fundamental Research Program (JCYJ20220531102407018).

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Authors and Affiliations

  1. Shenzhen University, Shenzhen, 518060, Guangdong, China

    Haosheng Su & Xuan Yang

  2. Guangdong Provincial Key Laboratory of Popular High Performance Computers, Shenzhen, Guangdong, China

    Haosheng Su & Xuan Yang

  3. Shenzhen Key Laboratory of Service Computing and Application, Shenzhen, Guangdong, China

    Haosheng Su & Xuan Yang

Authors
  1. Haosheng Su
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  2. Xuan Yang
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Correspondence to Xuan Yang .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen's University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Su, H., Yang, X. (2023). Nonuniformly Spaced Control Points Based on Variational Cardiac Image Registration. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_60

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  • DOI: https://doi.org/10.1007/978-3-031-43999-5_60

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  • Online ISBN: 978-3-031-43999-5

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