期刊
FRONTIERS IN NEUROSCIENCE
卷 12, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2018.00777
关键词
Alzheimer's disease; deep learning; convolutional neural networks; mild cognitive impairment; magnetic resonance imaging
资金
- National Key R&D Program of China [2017YFC0108703]
- National Natural Science Foundation of China [61871341, 61571380, 61811530021, 61672335, 61773124, 61802065, 61601276]
- Project of Chinese Ministry of Science and Technology [2016YFE0122700]
- Natural Science Foundation of Fujian Province of China [2018J06018, 2018J01565, 2016J05205, 2016J05157]
- Science and Technology Program of Xiamen [3502Z20183053]
- Fundamental Research Funds for the Central Universities [20720180056]
- Foundation of Educational and Scientific Research Projects for Young and Middle-aged Teachers of Fujian Province [JAT160074, JAT170406]
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.
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