Hippocampus Analysis by Combination of 3-D DenseNet and Shapes for Alzheimer's Disease Diagnosis

Hippocampus is one of the first involved regions in Alzheimer's disease (AD) and mild cognitive impairment (MCI), a prodromal stage of AD. Hippocampal atrophy is a validated, easily accessible, and widely used biomarker for AD diagnosis. Most of existing methods compute the shape and volume features for hippocampus analysis using structural magnetic resonance images (MRI). However, the regions adjacent to hippocampus may be relevant to AD, and the visual features of the hippocampal region are important for disease diagnosis. In this paper, we have proposed a new hippocampus analysis method to combine the global and local features of hippocampus by three-dimensional densely connected convolutional networks and shape analysis for AD diagnosis. The proposed method can make use of the local visual and global shape features to enhance the classification. Tissue segmentation and nonlinear registration are not required in the proposed method. Our method is evaluated with the T1-weighted structural MRIs from 811 subjects including 192 AD, 396 MCI (231 stable MCI and 165 progressive MCI), and 223 normal control in Alzheimer's disease neuroimaging initiative database. Experimental results show the proposed method achieves a classification accuracy of 92.29% and area under the ROC curve of 96.95% for AD diagnosis. Results comparison demonstrates the proposed method performs better than other methods.