Interpretation of Brain Morphology in Association to Alzheimer’s Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

Emanuel A. Azcona*, Pierre Besson, Yunan Wu, Arjun Punjabi, Adam Martersteck, Amil Dravid, Todd B. Parrish, S. Kathleen Bandt, Aggelos K. Katsaggelos

*Corresponding author for this work

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

Abstract

We propose a mesh-based technique to aid in the classification of Alzheimer’s disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer’s type.

Original languageEnglish (US)
Title of host publicationShape in Medical Imaging - International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMartin Reuter, Martin Reuter, Christian Wachinger, Hervé Lombaert, Hervé Lombaert, Beatriz Paniagua, Orcun Goksel, Islem Rekik
PublisherSpringer Science and Business Media Deutschland GmbH
Pages95-107
Number of pages13
ISBN (Print)9783030610555
DOIs
StatePublished - 2020
EventInternational Workshop on Shape in Medical Imaging, ShapeMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12474 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Shape in Medical Imaging, ShapeMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period10/4/2010/4/20

Keywords

  • Alzheimer’s disease classification
  • Graph convolutional networks
  • Neural network interpretability
  • Triangulated meshes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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