TY - CHAP
T1 - A Deep Generative Model of Neonatal Cortical Surface Development
AU - Fawaz, Abdulah
AU - Williams, Logan Z.J.
AU - Edwards, A. David
AU - Robinson, Emma C.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Cortical surfaces
KW - Geometric deep learning
KW - Neurodevelopment
UR - http://www.scopus.com/inward/record.url?scp=85135876120&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-12053-4_35
DO - 10.1007/978-3-031-12053-4_35
M3 - Conference paper
AN - SCOPUS:85135876120
SN - 9783031120527
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 469
EP - 481
BT - Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings
A2 - Yang, Guang
A2 - Aviles-Rivero, Angelica
A2 - Roberts, Michael
A2 - Schönlieb, Carola-Bibiane
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022
Y2 - 27 July 2022 through 29 July 2022
ER -