Conference Proceedings

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

D Petrov, BA Gutman, E Kuznetsov, CRK Ching, K Alpert, A Zavaliangos-Petropulu, D Isaev, JA Turner, TGM van Erp, L Wang, L Schmaal, D Veltman, PM Thompson

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Published : 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 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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