Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer's disease diagnosis.

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 194AD 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.