  A major challenge in medical image analysis is the automated detection of
biomarkers from neuroimaging data. Traditional approaches, often based on image
registration, are limited in capturing the high variability of cortical
organisation across individuals. Deep learning methods have been shown to be
successful in overcoming this difficulty, and some of them have even
outperformed medical professionals on certain datasets. In this paper, we apply
a deep neural network to analyse the cortical surface data of neonates, derived
from the publicly available Developing Human Connectome Project (dHCP). Our
goal is to identify neurodevelopmental biomarkers and to predict gestational
age at birth based on these biomarkers. Using scans of preterm neonates
acquired around the term-equivalent age, we were able to investigate the impact
of preterm birth on cortical growth and maturation during late gestation.
Besides reaching state-of-the-art prediction accuracy, the proposed model has
much fewer parameters than the baselines, and its error stays low on both
unregistered and registered cortical surfaces.
