Abstract
Hippocampal atrophy is often considered as one of the important biomarkers for early diagnosis of Alzheimer’s disease (AD), which is an irreversible neurodegenerative disorder. Traditional methods for hippocampus analysis usually computed the shape and volume features from structural Magnetic Resonance Image (sMRI) for the computer-aided diagnosis of AD as well as its prodromal stage, i.e., mild cognitive impairment (MCI). Motivated by the success of deep learning, this paper proposes a deep learning method with the multi-channel cascaded convolutional neural networks (CNNs) to gradually learn the combined hierarchical representations of hippocampal shapes and asymmetries from the binary hippocampal masks for AD classification. First, image segmentation is performed to generate the bilateral hippocampus binary masks for each subject and the mask difference is obtained by subtracting them. Second, multi-channel 3D CNNs are individually constructed on the hippocampus masks and mask differences to extract features of hippocampal shapes and asymmetries for classification. Third, a 2D CNN is cascaded on the 3D CNNs to learn high-level correlation features. Finally, the features learned by multi-channel and cascaded CNNs are combined with a fully connected layer followed by a softmax classifier for disease classification. The proposed method can gradually learn the combined hierarchical features of hippocampal shapes and asymmetries to enhance the classification. Our method is verified on the baseline sMRIs from 807 subjects including 194 AD patients, 397 MCI (164 progressive MCI (pMCI) + 233 stable MCI (sMCI)), and 216 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrate that the proposed method achieves an AUC (Area Under the ROC Curve) of 88.4%, 74.6% and 71.9% for AD vs. NC, MCI vs. NC and pMCI vs. sMCI classifications, respectively. It proves the promising classification performance and also shows that both hippocampal shape and asymmetry are helpful for AD diagnosis.
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Funding
This study was supported in part by the National Natural Science Foundation of China (NSFC) under grants (6181101049, 61981340415, 61773263), Natural Science Foundation of Shanghai (20ZR1426300), Shanghai Jiao Tong University Scientific and Technological Innovation Funds (2019QYB02), and an ECNU-SJTU joint grant from the Basic Research Project of Shanghai Science and Technology Commission (No.19JC1410102).
Data collection and sharing were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We just use the data from the ADNI dataset for this study. The ADNI investigators did not participate in the analysis or writing of this study. A complete list of ADNI investigators can be found online at http://adni.loni.usc.edu/about/governance/principal-investigators/.
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Li, A., Li, F., Elahifasaee, F. et al. Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Brain Imaging and Behavior 15, 2330–2339 (2021). https://doi.org/10.1007/s11682-020-00427-y
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DOI: https://doi.org/10.1007/s11682-020-00427-y