  As very large studies of complex neuroimaging phenotypes become more common,
human quality assessment of MRI-derived data remains one of the last major
bottlenecks. Few attempts have so far been made to address this issue with
machine learning. In this work, we optimize predictive models of quality for
meshes representing deep brain structure shapes. We use standard vertex-wise
and global shape features computed homologously across 19 cohorts and over 7500
human-rated subjects, training kernelized Support Vector Machine and Gradient
Boosted Decision Trees classifiers to detect meshes of failing quality. Our
models generalize across datasets and diseases, reducing human workload by
30-70\%, or equivalently hundreds of human rater hours for datasets of
comparable size, with recall rates approaching inter-rater reliability.
