  We present several deep learning models for assessing the morphometric
fidelity of deep grey matter region models extracted from brain MRI. We test
three different convolutional neural net architectures (VGGNet, ResNet and
Inception) over 2D maps of geometric features. Further, we present a novel
geometry feature augmentation technique based on a parametric spherical
mapping. Finally, we present an approach for model decision visualization,
allowing human raters to see the areas of subcortical shapes most likely to be
deemed of failing quality by the machine. Our training data is comprised of
5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset
contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our
final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and
Inception for all of our predictive tasks.
