A Deep Generative Model of Neonatal Cortical Surface Development

Abdulah Fawaz*, Logan Z.J. Williams, A. David Edwards, Emma C. Robinson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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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.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings
EditorsGuang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031120527
Publication statusPublished - 2022
Event26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022 - Cambridge, United Kingdom
Duration: 27 Jul 202229 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13413 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022
Country/TerritoryUnited Kingdom


  • Cortical surfaces
  • Geometric deep learning
  • Neurodevelopment


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