  Brain morphometry from magnetic resonance imaging (MRI) is a consolidated
biomarker for many neurodegenerative diseases. Recent advances in this domain
indicate that deep convolutional neural networks can infer morphometric
measurements within a few seconds. Nevertheless, the accuracy of the devised
model for insightful bio-markers (mean curvature and thickness) remains
unsatisfactory. In this paper, we propose a more accurate and efficient neural
network model for brain morphometry named HerstonNet. More specifically, we
develop a 3D ResNet-based neural network to learn rich features directly from
MRI, design a multi-scale regression scheme by predicting morphometric measures
at feature maps of different resolutions, and leverage a robust optimization
method to avoid poor quality minima and reduce the prediction variance. As a
result, HerstonNet improves the existing approach by 24.30% in terms of
intraclass correlation coefficient (agreement measure) to FreeSurfer
silver-standards while maintaining a competitive run-time.
