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Annual Conference on Medical Image Understanding and Analysis

MIUA 2022: Medical Image Understanding and Analysis pp 469–481Cite as

A Deep Generative Model of Neonatal Cortical Surface Development

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13413)

Abstract

The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional filters are inappropriate on non-flat topologies. To close this gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to translate sphericalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between different stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation (>37 weeks gestation), validated through comparison with a trained term/preterm classifier. Simulated differences in cortical maturation are consistent with observations in the literature.

Keywords

  • Geometric deep learning
  • Cortical surfaces
  • Neurodevelopment

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Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Abdulah Fawaz, Logan Z. J. Williams & Emma C. Robinson

  2. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Logan Z. J. Williams, A. David Edwards & Emma C. Robinson

  3. Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK

    A. David Edwards

  4. MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, UK

    A. David Edwards

Authors
  1. Abdulah Fawaz
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  2. Logan Z. J. Williams
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  4. Emma C. Robinson
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Corresponding author

Correspondence to Abdulah Fawaz .

Editor information

Editors and Affiliations

  1. Imperial College London, London, UK

    Guang Yang

  2. University of Cambridge, Cambridge, UK

    Angelica Aviles-Rivero

  3. University of Cambridge, Cambridge, UK

    Michael Roberts

  4. University of Cambridge, Cambridge, UK

    Carola-Bibiane Schönlieb

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Fawaz, A., Williams, L.Z.J., Edwards, A.D., Robinson, E.C. (2022). A Deep Generative Model of Neonatal Cortical Surface Development. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_35

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  • DOI: https://doi.org/10.1007/978-3-031-12053-4_35

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