RT Journal Article
SR Electronic
T1 Development and Validation of a Deep Learning–Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 2227
OP 2234
DO 10.3174/ajnr.A6848
VO 41
IS 12
A1 Suh, C.H.
A1 Shim, W.H.
A1 Kim, S.J.
A1 Roh, J.H.
A1 Lee, J.-H.
A1 Kim, M.-J.
A1 Park, S.
A1 Jung, W.
A1 Sung, J.
A1 Jahng,, G.-H.
A1 for the Alzheimer’s Disease Neuroimaging Initiative
YR 2020
UL http://www.ajnr.org/content/41/12/2227.abstract
AB BACKGROUND AND PURPOSE: Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning–based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images.MATERIALS AND METHODS: A deep learning–based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls.RESULTS: In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758–0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668–0.870. XGBoost had a sensitivity of 79% and a specificity of 80%.CONCLUSIONS: The deep learning–based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.ADAlzheimer diseaseADNIAlzheimer’s Disease Neuroimaging InitiativeAUCarea under the curveCNNconvolutional neural networkMCImild cognitive impairmentOASISOpen Access Series of Imaging StudiesSVMSupport Vector Machine