Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Boris A. Gutman,
Shih-Hua (Julie) Yu,
Theo G.M. van Erp,
Jessica A. Turner,
Christopher RK Ching,
Vince D. Calhoun,
Ole Andreas Andreasen,
Steven G Potkin,
Pedro G.P. Rosa,
Bernhard T. Baune,
Ian H. Gotlib,
Jair C. Soares,
Ruben C. Gur,
N. Trung Doan,
Lars T. Westlye,
Dan J. Stein,
Erin W. Dickie,
Erick Jorge Canales-Rodríguez,
Alexander J. Huang,
Marcus V. Zanetti,
Mauricio H. Serpa,
Matthew D. Sacchet,
Ilya M. Veer,
for the ENIGMA consortium
Posted 21 Jul 2017
bioRxiv DOI: 10.1101/166496 (published DOI: 10.1007/978-3-319-67389-9_43)
Posted 21 Jul 2017
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.
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