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As very large studies of complex neuroimaging phenotypes become more common,human quality assessment of MRI-derived data remains one of the last majorbottlenecks. Few attempts have so far been made to address this issue withmachine learning. In this work, we optimize predictive models of quality formeshes representing deep brain structure shapes. We use standard vertex-wiseand global shape features computed homologously across 19 cohorts and over 7500human-rated subjects, training kernelized Support Vector Machine and GradientBoosted Decision Trees classifiers to detect meshes of failing quality. Ourmodels generalize across datasets and diseases, reducing human workload by30-70\%, or equivalently hundreds of human rater hours for datasets ofcomparable size, with recall rates approaching inter-rater reliability.

Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging