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

Dmitry Petrov*, Boris A. Gutman, Egor Kuznetsov, Christopher R.K. Ching, Kathryn Alpert, Artemis Zavaliangos-Petropulu, Dmitry Isaev, Jessica A. Turner, Theo G.M. van Erp, Lei Wang, Lianne Schmaal, Dick Veltman, Paul M. Thompson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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 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.

Original languageEnglish (US)
Title of host publicationShape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsHervé Lombaert, Beatriz Paniagua, Bernhard Egger, Marcel Lüthi, Martin Reuter, Christian Wachinger
PublisherSpringer Verlag
Pages268-276
Number of pages9
ISBN (Print)9783030047467
DOIs
StatePublished - Jan 1 2018
EventInternational Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 20 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11167 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/20/189/20/18

Keywords

  • Deep learning
  • Quality checking
  • Subcortical shape analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Deep Learning for Quality Control of Subcortical Brain 3D Shape Models'. Together they form a unique fingerprint.

  • Cite this

    Petrov, D., Gutman, B. A., Kuznetsov, E., Ching, C. R. K., Alpert, K., Zavaliangos-Petropulu, A., Isaev, D., Turner, J. A., van Erp, T. G. M., Wang, L., Schmaal, L., Veltman, D., & Thompson, P. M. (2018). Deep Learning for Quality Control of Subcortical Brain 3D Shape Models. In H. Lombaert, B. Paniagua, B. Egger, M. Lüthi, M. Reuter, & C. Wachinger (Eds.), Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 268-276). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11167 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04747-4_25