Deep Learning for Quality Control of Subcortical Brain 3D Shape Models
The ENIGMA Consortium,
Boris A. Gutman,
Theo G.M. van Erp,
Jessica A. Turner,
Christopher R.K. Ching,
Theodore D. Satterthwaite,
Ole Andreas Andreassen,
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-Rodrguez,
Alexander J. Huang,
Marcus V. Zanetti,
Mauricio H. Serpa,
Matthew D. Sacchet,
Ilya M. Veer,
Paul M. Thompson,
for the ENIGMA consortium
Posted 28 Aug 2018
bioRxiv DOI: 10.1101/402255
Posted 28 Aug 2018
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.
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