3D CNN based Alzheimer’s diseases classification using segmented Grey matter extracted from whole-brain MRI

Bijen Khagi - Department of Information and Communication Engineering, Chosun University, Dong-Gu, Gwangju 501-759, Republic of Korea
Goo-Rak Kwon - Department of Information and Communication Engineering, Chosun University, Dong-Gu, Gwangju 501-759, Republic of Korea


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.5.2.572

Abstract


A recent study from MRI has revealed that there is a minor increase in cerebral-spinal fluid (CSF) content in brain ventricles and sulci, along with a substantial decrease in grey matter (GM) content and brain volume among Alzheimer's disease (AD) patients. It has been discovered that the grey matter volume shrinkage may indicate the possible case of dementia and related diseases like AD. Clinicians and radiologists use imaging techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scan, and Positron Emission Tomography (PET) to diagnose and visualize the tissue contents of the brain. Using the whole brain MRI as the feature is an on-going approach among machine learning researchers, however, we are interested only in grey matter content. First, we segment the MRI using the SPM (Statistical parameter mapping) tool and then apply the smoothing technique to get a 3D image of grey matter (later called as grey version) from each MRI. This image file is then fed into 3D convolutional neural network (CNN) with necessary pre-processing so that it can train the network, to produce a classifying model. Once trained, an untested MRI (i.e. its grey version) can be passed through the CNN to determine whether it is a healthy control (HC), or Mild Cognitive Impairment (MCI) due to AD (mAD) or AD dementia (ADD). Our validation and testing accuracy are reported here and compared with normal MRI and its grey version.

Keywords


Alzheimer disease (AD); Magnetic resonance imaging (MRI); Statistical parameter mapping (SPM); Convolutional neural network (CNN).

Full Text:

PDF

References


J. L. Tanabe, D. Amend, N. Schuff, V. DiSclafani, F. Ezekiel, D. Norman, G. Fein, and M. W. Weiner, “Tissue segmentation of the brain in Alzheimer diseaseâ€, American Journal of Neuroradiology. 1997; 18(1):115-123.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networksâ€, Proc. NIPS, 2012; 1097–1105.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutionsâ€, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2015; 1–9.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition†arXiv:1512.03385, 2015

Girshick, Ross, “Fast r-cnn,†In Proceedings of the IEEE international conference on computer vision, pp. 1440-1448. 2015.

National Research Centre for Dementia, Korea http://www.nrcd.re.kr/

J. Ashburner, “A fast diffeomorphic image registration algorithmâ€, NeuroImage, Volume 38, Issue 1, 2007, Pages 95-113, ISSN1053-8119, https://doi.org/10.1016/j.neuroimage.2007.07.007

Mazziotta J, Toga A, Evans A, et al, “A probabilistic atlas and reference system for the human brainâ€. International Consortium for Brain Mapping (ICBM), Philosophical Transactions of the Royal Society of London Series B. 2001; 356(1412):1293-1322. doi:10.1098/rstb.2001.0915.

Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T. and Ronneberger, O., 2016, October. “3D U-Net: learning dense volumetric segmentation from sparse annotationâ€. In International conference on medical image computing and computer-assisted intervention (pp. 424-432). Springer, Cham.

Alzheimer's Disease Neuroimaging Initiative: ADNI www.loni.ucla.edu/ADNI.

Y. Zhang, S. Wang, G. Ji, and Z. Dong “Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging,†IEEJ Transactions on Electrical and Electronic Engineering. 2015; 10(1):116-117.

Z. Dong, Y. Zhang, F. Liu, Y. Duan, A. Kangarlu, B. S. Peterson, “Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the spread technique,†NMR in Biomedicine. 2014; 27(11):1325-1332

J. Ashburner, A fast diffeomorphic image registration algorithm, NeuroImage, Volume 38, Issue 1, 2007, Pages 95-113, ISSN1053-8119, https://doi.org/10.1016/j.neuroimage.2007.07.007

Khagi, B. and Kwon, G.R., 2020. 3D CNN design for the classification of Alzheimer’s disease using brain MRI and PET. IEEE Access.

Nagi, J., Ducatelle, F., Di Caro, G.A., CireÅŸan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J. and Gambardella, L.M., 2011, November. “Max-pooling convolutional neural networks for vision-based hand gesture recognitionâ€. In 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 342-347). IEEE.

Nair, V. and Hinton, G.E., 2010, January. “Rectified linear units improve restricted Boltzmann machinesâ€. In Icml. Bishop, C.M., 2006. Pattern recognition and machine learning. springer.

Payan, Adrien, and Giovanni Montana. "Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks." arXiv preprint arXiv:1502.02506 (2015).

Hosseini-Asl, Ehsan, Georgy Gimel'farb, and Ayman El-Baz. "Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network." arXiv preprint arXiv:1607.00556 (2016).

Oh, Kanghan, Young-Chul Chung, Ko Woon Kim, Woo-Sung Kim, and Il-Seok Oh. "Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning." Scientific Reports 9, no. 1 (2019): 1-16.

Liu, M., Cheng, D., Wang, K., & Wang, Y. (2018). Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis. Neuroinformatics, 16(3-4), 295–308. doi:10.1007/s12021-018-9370-4.

Yang G, Zhang Y, Yang J, et al. Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl. 2015;75(23):15601-15617.

Jha, Debesh, Ji-In Kim, and Goo-Rak Kwon. "Diagnosis of Alzheimer’s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network." Journal of healthcare engineering 2017 (2017).

Khagi, Bijen, Gooâ€Rak Kwon, and Ramesh Lama. "Comparative analysis of Alzheimer's disease classification by CDR level using CNN, feature selection, and machineâ€learning techniques." International Journal of Imaging Systems and Technology 29, no. 3 (2019): 297-310.

Wang SH, Zhang Y, Li YJ, Jia WJ, Liu1 FY, Yang MM. Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed Tools Appl. 2016;77:1-25

Maas, Andrew L., Awni Y. Hannun, and Andrew Y. Ng. "Rectifier nonlinearities improve neural network acoustic models." In Proc. icml, vol. 30, no. 1, p. 3. 2013.