Abstract

Background
A practical screening tool to detect Alzheimer’s disease (AD) based on brain MRI would be valuable. Here we tested a deep learning method for subject‐wise AD classification; as gray matter (GM) is preferentially affected by AD, we also performed MRI tissue classification on the input data, to test the added value of these input features. We set out to compare different types of imaging data types1 as inputs to a 3D Convolutional Neural Network (CNN) for the AD classification task.
Method
We analyzed T1‐weighted brain MRI scans from 1123 subjects (596M/527F, 55.2 ‐ 95.8 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). After registering the T1‐w MRI scans to a common brain template, GM was segmented as shown in Figure 1. We subdivided the dataset into training (3,302 scans/853 subjects), validation (413 scans/100 subjects) and test (170 scans/170 subjects) data for the …

Date

January 1, 1970

Authors

Nikhil J Dhinagar, Sophia I Thomopoulos, Conor Owens‐Walton, Dimitris Stripelis, Jose Luis Ambite, Greg Ver Steeg, Paul M Thompson

Journal

Alzheimer's & Dementia

Volume

18

Pages

e066446