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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 pp 39–47Cite as

Placental Flattening via Volumetric Parameterization

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11767)

Abstract

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our implementation is freely available at https://github.com/mabulnaga/placenta-flattening.

Keywords

  • Placenta
  • Fetal MRI
  • Flattening
  • Injective maps
  • Volumetric mesh parameterization
  • Anatomy visualization

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Acknowledgments

This work was supported in part by NIH NIBIB NAC P41EB015902, NIH NICHD U01HD087211, NSF IIS-1838071, Air Force FA9550-19-1-0319, Wistron, SIP, AWS, NSF Graduate Research Fellowship, and NSERC Post Graduate Scholarship.

Author information

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA

    S. Mazdak Abulnaga, Justin Solomon & Polina Golland

  2. Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Esra Abaci Turk & P. Ellen Grant

  3. Department of Computer Science and Operations Research, Université de Montréal, Montréal, QC, Canada

    Mikhail Bessmeltsev

Authors
  1. S. Mazdak Abulnaga
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  2. Esra Abaci Turk
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  3. Mikhail Bessmeltsev
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  4. P. Ellen Grant
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  5. Justin Solomon
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  6. Polina Golland
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Corresponding author

Correspondence to S. Mazdak Abulnaga .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Prof. Tianming Liu

  3. Western University, London, ON, Canada

    Dr. Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Prof. Dr. Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

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Abulnaga, S.M., Abaci Turk, E., Bessmeltsev, M., Grant, P.E., Solomon, J., Golland, P. (2019). Placental Flattening via Volumetric Parameterization. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_5

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  • DOI: https://doi.org/10.1007/978-3-030-32251-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32250-2

  • Online ISBN: 978-3-030-32251-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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