Journal article

Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

undefined The ENIGMA Consortium, Dmitry Petrov, Boris Gutman, Egor Kuznetsov, Theo GM van Erp, Jessica Turner, Lianne Schmaal, Dick Veltman, Lei Wang, Kathryn Alpert, Dmitry Isaev, Artemis Zavaliangos-Petropulu, Christopher RK Ching, Vince Calhoun, David Glahn, Theodore Satterthwaite, Ole Andreas Andreassen, Stefan Borgwardt, Fleur Howells, Nynke Groenewold Show all

Published : 2018

Abstract

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 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 f..

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