Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images

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...

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Veröffentlicht in:American journal of neuroradiology : AJNR Jg. 41; H. 12; S. 2227
Hauptverfasser: Suh, C H, Shim, W H, Kim, S J, Roh, J H, Lee, J-H, Kim, M-J, Park, S, Jung, W, Sung, J, Jahng, G-H
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.12.2020
Schlagworte:
Aged
Aged, 80 and over
Algorithms
Alzheimer Disease - diagnostic imaging
Cognitive Dysfunction - diagnostic imaging
Deep Learning
Female
Humans
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Magnetic Resonance Imaging - methods
Male
Middle Aged
Neuroimaging - methods
ISSN:1936-959X, 1936-959X
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Zusammenfassung: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. 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. 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 ( < .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%. 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.
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ISSN:1936-959X
1936-959X
DOI:10.3174/ajnr.A6848