Abstract
Accurate and early diagnosis of Alzheimer’s disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.
Similar content being viewed by others
References
Adrien, P.A.G.M. (2015). Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv:1502.02506 [cs.CV].
Alberdi, A., Aztiria, A., & Basarab, A. (2016). On the early diagnosis of Alzheimer's disease from multimodal signals: A survey. Artificial Intelligence in Medicine, 71, 1–29.
Cabral, C., Silveira, M., Neuroimaging, A.S.D. (2013). Classification of Alzheimer’s disease from FDG-PET images using favourite class ensembles. 2013 35th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), pp. 2477–2480.
Cheng, B., Liu, M., Suk, H. I., Shen, D., & Zhang, D. (2015). Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging and Behavior, 9, 913–926.
Gerardin, E., Chetelat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H. S., Niethammer, M., Dubois, B., Lehericy, S., Garnero, L., Eustache, F., Colliot, O., & Initi, A.s. D. N. (2009). Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. NeuroImage, 47, 1476–1486.
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. pp. 770–778.
Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M. K., & Johnson, S. C. (2009). Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. NeuroImage, 48, 138–149.
Hosseini-Asl, E., Keynton, R., & El-Baz, A. (2016). Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. 2016 I.E. International Conference on Image Processing (ICIP), pp 126–130.
Ishii, K., Kawachi, T., Sasaki, H., Kono, A. K., Fukuda, T., Kojima, Y., & Mori, E. (2005). Voxel-based morphometric comparison between early- and late-onset mild Alzheimer’s disease and assessment of diagnostic performance of z score images. AJNR American Journal of Neuroradiology, 26(2), 333–340.
Jack Jr., C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L Whitwell, J., Ward, C., Dale, A. M., Felmlee, J. P., Gunter, J. L., Hill, D. L., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C. S., Krueger, G., Ward, H. A., Metzger, G. J., Scott, K. T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J. P., Fleisher, A. S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., & Weiner, M. W. (2008). The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging: JMRI, 27, 685–691.
Kabani, N., MacDonald, D., Holmes, C. J., & Evans, A. (1998). A 3D atlas of the human brain. NeuroImage, 7, S717.
Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack Jr, C.R., Ashburner, J., & Frackowiak, R.S.J. (2008). Automatic classification of MR scans in Alzheimer’s disease Brain 131(Pt 3):681–689.
Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, pp. 1097–1105.
Lécun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc IEEE, 86, 2278–2324.
Lerch, J. P., Pruessner, J., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H., & Evans, A. C. (2008). Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. Neurobiology of Aging, 29, 23–30.
Li, R., Zhang, W., Suk, H.I., Wang, L., Li, J., Shen, D., Ji, S., (2014). Deep learning based imaging data completion for improved brain disease diagnosis. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 17, 305–312.
Lin, T.Y., Roychowdhury, A., & Maji, S. (2015). Bilinear CNN models for fine-grained visual recognition. IEEE International Conference on Computer Vision, Santiago, Chile, pp 1449–1457.
Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., & Fulham, M. J. (2015). Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Transactions on Biomedical Engineering, 62, 1132–1140.
Lu, S., Xia, Y., Cai, T.W., & Feng, D.D. (2015). Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging. 2015 37th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), pp. 2251–2254.
Minati, L., Edginton, T., Bruzzone, M. G., & Giaccone, G. (2009). Reviews: Current concepts in Alzheimer's disease: A multidisciplinary review. American Journal of Alzheimers Disease & Other Dementias, 24, 95–121.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221.
Silveira M, Marques, J. (2010). Boosting Alzheimer disease diagnosis using PET images. 20th IEEE international conference on pattern recognition (ICPR), pp. 2556–2559.
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging, 17, 87–97.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
Suk, H.I., Shen, D., 2013. Deep learning-based feature representation for AD/MCI classification. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 16, 583–590.
Suk, H. I., Lee, S. W., & Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569–582.
Suk, H. I., Lee, S. W., & Shen, D. (2015). Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure and Function, 220, 841–859.
Wang, Y., Nie, J., Yap, P. T., Shi, F., Guo, L., & Shen, D. (2011). Robust deformable-surface-based skull-stripping for large-scale studies. Medical Image Computing and Computer-Assisted Intervention – MICCAI, 14(3), 635–642.
Wang, Y., Zhang, P., An, L., Ma, G., Kang, J., Shi, F., Wu, X., Zhou, J., Lalush, D. S., & Lin, W. (2016). Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. Physics in Medicine and Biology, 61(2), 791–812.
Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2015). Learning to track for spatio-temporal action localization. pp. 3164–3172.
Yan, W., Ma, G., Le, A., Feng, S., Pei, Z., Xi, W., Zhou, J., & Shen, D. (2017). Semi-supervised tripled dictionary learning for standard-dose PET image prediction using low-dose PET and multimodal MRI. IEEE Transactions on Biomedical Engineering, 64, 569–579.
Zeiler, M.D. (2012). ADADELTA: An adaptive learning rate method. Computer Science.
Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. Basel: Springer International Publishing.
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011). Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55, 856–867.
Acknowledgments
This work was supported in part by National Natural Science Foundation of China (NSFC) under grants (No. 61375112, 61773263, U1504606), The National Key Research and Development Program of China (No.2016YFC0100903) and SMC Excellent Young Faculty program of SJTU. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (NIH Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co.,Medpace, Inc.,Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles.
Author information
Authors and Affiliations
Consortia
Corresponding authors
Additional information
Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a Group/Institutional Author.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.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.ucla.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Rights and permissions
About this article
Cite this article
Liu, M., Cheng, D., Wang, K. et al. Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis. Neuroinform 16, 295–308 (2018). https://doi.org/10.1007/s12021-018-9370-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-018-9370-4