Construction of MRI-Based Alzheimer’s Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset
Affiliations: [a] School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada
| [b] Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
Correspondence:
[*]
Correspondence to: Mirza Faisal Beg and Da Ma, ASB 8857, 8888 University Drive, Simon Fraser University, Burnaby, BC, V5A1S6, Canada. E-mails: faisal-lab@sfu.ca; da_ma@sfu.ca.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Note: [2] Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (http://www.loni.usc.edu/ADNI). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at http://www.aibl.csiro.au
Abstract: Background:In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer’s disease. Due to memory constraints, many of the proposed CNNs work at a 2D slice-level or 3D patch-level. Objective:Here, we propose a subject-level 3D CNN that can extract the neurodegenerative patterns of the whole brain MRI and converted into a probabilistic Dementia score. Methods:We propose an efficient and lightweight subject-level 3D CNN featuring dilated convolutions. We trained our network on the ADNI data on stable Dementia of the Alzheimer’s type (sDAT) from stable normal controls (sNC). To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other sites (AIBL), images acquired using different protocols (OASIS), and longitudinal images acquired over a short period of time (MIRIAD). Results:We achieved a 5-fold cross-validated balanced accuracy of 88%in differentiating sDAT from sNC, and an overall specificity of 79.5%and sensitivity 79.7%on the entire set of 7,902 independent test images. Conclusion:Independent testing is essential for estimating the generalization ability of the network to unseen data, but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. Our comprehensive evaluation highlighting the competitive performance of our network and potential promise for generalization.
Keywords: 3D CNN, dementia of Alzheimer’s type (DAT), magnetic resonance imaging
