Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment.

Aged Aged, 80 and over Alzheimer Disease / pathology Brain / diagnostic imaging Cognitive Dysfunction / pathology Female Humans Magnetic Resonance Imaging / methods Male Neural Networks, Computer
Alzheimer's Disease Deep learning Machine learning Mild Cognitive Impairment Siamese networks Structural magnetic resonance imaging

Journal

Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883

Informations de publication

Date de publication:
12 2019
Historique:
received: 04 01 2019
revised: 01 07 2019
accepted: 02 07 2019
pubmed: 19 7 2019
medline: 6 5 2020
entrez: 19 7 2019
Statut: ppublish

Résumé

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.

Identifiants

DOI: 10.1016/j.mri.2019.07.003 PMID: 31319126 PMC: PMC6874905
pubmed: 31319126
pii: S0730-725X(19)30008-6
doi: 10.1016/j.mri.2019.07.003
pmc: PMC6874905
mid: NIHMS1048461
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

190-199

Subventions

Organisme : NIA NIH HHS
ID : P30 AG066507
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG024904
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG033655
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB020062
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG033655
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015909
Pays : United States

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

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Auteurs

Chin-Fu Liu (CF)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: cliu104@jhu.edu.

Shreyas Padhy (S)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: shreyas@jhu.edu.

Sandhya Ramachandran (S)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

Victor X Wang (VX)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

Andrew Efimov (A)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.

Alonso Bernal (A)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

Linyuan Shi (L)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.

Marc Vaillant (M)

Animetrics Inc., Conway, NH, USA.

J Tilak Ratnanather (JT)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

Andreia V Faria (AV)

Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD.

Brian Caffo (B)

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.

Marilyn Albert (M)

Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.

Michael I Miller (MI)

Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA. Electronic address: mim@cis.jhu.edu.

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Classifications MeSH

questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Using deep Siamese neural networks for...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Sujet âgé de 80 ans ou plus › Using deep Siamese neural networks for...
questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Démence › Maladie d'Alzheimer › Using deep Siamese neural networks for...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Using deep Siamese neural networks for...
questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Troubles de la cognition › Dysfonctionnement cognitif › Using deep Siamese neural networks for...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Using deep Siamese neural networks for...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Using deep Siamese neural networks for...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Using deep Siamese neural networks for...