Vol. 171
Latest Volume
All Volumes
PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2021-08-12
Multimodal 2.5D Convolutional Neural Network for Diagnosis of Alzheimer's Disease with Magnetic Resonance Imaging and Positron Emission Tomography
By
Progress In Electromagnetics Research, Vol. 171, 21-34, 2021
Abstract
Alzheimer's disease (AD) is a degenerative disease of the nervous system that often occurs in the elderly. As magnetic resonance imaging (MRI) and positron emission tomography (PET) reflect the brain's anatomical changes and functional changes caused by AD, they are often used to diagnose AD. Multimodal fusion based on these two types of images can effectively utilize complementary information and improve diagnostic performance. To avoid the computational complexity of the 3D image and expand training samples, this study designed an AD diagnosis framework based on a 2.5D convolutional neural network (CNN) to fuse multimodal data. First, MRI and PET were preprocessed with skull stripping and registration. After that, multiple 2.5D patches were extracted within the hippocampus regions from both MRI and PET. Then, we constructed a multimodal 2.5D CNN to integrate the multimodal information from MRI and PET patches. We also utilized a training strategy called branches pre-training to enhance the feature extraction ability of the 2.5D CNN by pre-training two branches with corresponding modalities individually. Finally, the results of patches are used to diagnose AD and progressive mild cognitive impairment (pMCI) patients from normal controls (NC). The experiments were conducted with the ADNI dataset, and accuracies of 92.89% and 84.07% were achieved in the AD vs. NC and pMCI vs. NC tasks. The results are much better than using single modality and indicate that the proposed multimodal 2.5D CNN could effectively integrate complementary information from multi-modality and yield a promising AD diagnosis performance.
Citation
Xuyang Zhang, Weiming Lin, Min Xiao, and Huazhi Ji, "Multimodal 2.5D Convolutional Neural Network for Diagnosis of Alzheimer's Disease with Magnetic Resonance Imaging and Positron Emission Tomography," Progress In Electromagnetics Research, Vol. 171, 21-34, 2021.
doi:10.2528/PIER21051102
References

1. Todd, S., S. Barr, M. Roberts, and A. P. Passmore, "Survival in dementia and predictors of mortality: A review," International Journal of Geriatric Psychiatry, Vol. 28, No. 11, 1109-1124, 2013.

2. Markesbery, W. R. and M. A. Lovell, "Neuropathologic alterations in mild cognitive impairment: A review," Journal of Alzheimer's Disease, Vol. 19, No. 1, 221-228, 2010.

3. Tong, T., Q. Gao, R. Guerrero, C. Ledig, and D. Rueckert, "A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease," IEEE Transactions on Biomedical Engineering, Vol. 64, No. 1, 1, 2016.

4. Eskilden, S. F., P. Coupé, D. García-Lorenzo, V. Fonov, J. C. Pruessner, and D. L. Collins, "Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning," Neuroimage, Vol. 65, 511-521, 2013.

5. Tong, T., R. Wolz, Q. Gao, and R. Guerrero, "Multiple instance learning for classification of dementia in brain MRI," Medical Image Analysis, Vol. 18, No. 5, 808-818, 2014.

6. Drzezga, A., D. Altomare, C. Festari, J. Arbizu, S. Orini, K. Herholz, P. Nestor, F. Agosta, F. Bouwman, and F. Nobili, "Diagnostic utility of 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) in asymptomatic subjects at increased risk for Alzheimer's disease," European Journal of Nuclear Medicine and Molecular Imaging, Vol. 45, No. 9, 1487-1496, 2018.

7. Liu, M., D. Cheng, and W. Yan, "Classification of Alzheimer's disease by combination of convolutional and recurrent neural networks using FDG-PET images," Frontiers in Neuroinformatics, Vol. 12, 35, 2018.

8. Alberdi, A., A. Aztiria, and A. Basarab, "On the early diagnosis of Alzheimer's disease from multimodal signals: A survey," Artificial Intelligence in Medicine, Vol. 71, 1-29, 2016.

9. Mzoughi, H., I. Njeh, A. Wali, et al. "Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification," Journal of Digital Imaging, Vol. 33, 903-915, 2020.

10. Gao, Y., Z. Li, C. Song, et al. "Automatic rat brain image segmentation using triple cascaded convolutional neural networks in a clinical PET/MR," Physics in Medicine and Biology, Vol. 66, No. 4, 04NT01, 2021.

11. Zhang, Q., Y. Liao, X. Wang, et al. "A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy," European Journal of Nuclear Medicine and Molecular Imaging, Vol. 48, 2476-2485, 2021.

12. Zhang, R., C. Cheng, X. Zhao, and X. Li, "Multiscale mask R-CNN-based lung tumor detection using PET imaging," Molecular Imaging, Vol. 18, 1-8, 2019.

13. Zheng, H., L. Qian, Y. Qin, et al. "Improving the slice interaction of 2.5D CNN for automatic pancreas segmentation," Medical Physics, Vol. 47, 5543-5554, 2020.

14. Kitrungrotsakul, T., X. Han, Y. Iwamoto, et al. "A cascade of 2.5D CNN and bidirectional CLSTM network for mitotic cell detection in 4D microscopy image," IEEE-Acm Transactions on Computational Biology and Bioinformatics, Vol. 18, 396-404, 2021.

15. Li, A., F. Li, F. Elahifasaee, M. Liu, and L. Zhang, "Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer's disease diagnosis," Brain Imaging and Behavior, 1-10, 2021.

16. Jo, T., K. Nho, S. L. Risacher, and A. J. Saykin, "Deep learning detection of informative features in tau PET for Alzheimer's disease classification," BMC Bioinformatics, Vol. 21, No. Suppl 21, 496, 2020.

17. Gao, X., R. Hui, Z. Tian, et al. "Classification of CT brain images based on deep learning networks," Computer Methods and Programs in Biomedicine, Vol. 138, 49-56, 2017.

18. Suk, H. I., S. W. Lee, and D. Shen, "Latent feature representation with stacked auto-encoder for AD/MCI diagnosis," Brain Structure and Function, Vol. 220, No. 2, 841-859, 2015.

19. Zhang, D., Y. Wang, L. Zhou, H. Yuan, and D. Shen, "Multimodal classification of Alzheimer's disease and mild cognitive impairment," NeuroImage, Vol. 55, No. 3, 856-867, 2011.

20. Huang, Y., J. Xu, Y. Zhou, and T. Tong, "Diagnosis of Alzheimer's disease via multi-modality 3D convolutional neural network," Frontiers in Neuroscience, Vol. 13, 509, 2019.

21. Cheng, D. and M. Liu, "CNNs based multi-modality classification for AD diagnosis," 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1-5, 2017.

22. Zhou, P., S. Jiang, L. Yu, Y. Feng, C. Chen, and F. Li, "Use of a sparse-response deep belief network and extreme learning machine to discriminate Alzheimer's disease, mild cognitive impairment, and normal controls based on amyloid PET/MRI images," Frontiers in Medicine, Vol. 7, 987, 2021.

23. Zhu, X., H. Suk, and D. Shen, "Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification," World Wide Web, Vol. 22, No. 2, 907-925, 2019.

24. Lin, W., Q. Gao, J. Yuan, Z. Chen, and C. Feng, "Predicting Alzheimer's disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data," Frontiers in Aging Neuroscience, Vol. 12, 77, 2020.

25. Giorgio, A., L. Santelli, V. Tomassini, R. Bosnell, S. Smith, N. D. Stefano, and H. Johansen-Berg, "Age-related changes in grey and white matter structure throughout adulthood," NeuroImage, Vol. 51, No. 3, 943-951, 2010.

26. Dukart, J., M. L. Schroeter, and K. Muller, "Age correction in dementia-matching to a healthy brain," PloS One, Vol. 6, No. 7, e22193, 2011.

27. Lin, W., T. Tong, Q. Gao, D. Guo, X. Du, Y. Yang, G. Guo, M. Xiao, M. Du, and X. Qu, "Convolutional neural networks-based MRI image analysis for the Alzheimer's disease prediction from mild cognitive impairment," Frontiers in Neuroscience, Vol. 12, 777, 2018.

28. Roth, H. R., L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, "Improving computer-aided detection using convolutional neural networks and random view aggregation," IEEE Transactions on Medical Imaging, Vol. 35, No. 5, 1170-1181, 2016.

29. Han, X., J. Jovicich, D. Salat, A. Kouwe, B. Quinn, S. Czanner, et al. "Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of eld strength, scanner upgrade and manufacturer," NeuroImage, Vol. 32, No. 1, 180-194, 2006.

30. Lin, M., Q. Chen, and S. Yan, Network in network, Proceedings of the IEEE International Conference on Learning Representations, 2014.

31. Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al. "Going deeper with convolutions," The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9, 2015.

32. He, K., X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778, 2016.

33. Ioffe, S. and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," Proceedings of the 32nd International Conference on International Conference on Machine Learning, Vol. 37, 448-456, 2015.

34. Elahifasaee, F., F. Li, and M. Yang, "A classification algorithm by combination of feature decomposition and kernel discriminant analysis (KDA) for automatic MR brain image classification and AD diagnosis," Computational and Mathematical Methods in Medicine, Vol. 2019, 1-14, 2019.

35. Oh, K., Y. C. Chung, K. W. Kim, and I. S. Oh, "Classification and visualization of Alzheimer's disease using volumetric convolutional neural network and transfer learning," Scientific Reports, Vol. 9, No. 1, 18150-18165, 2019.

36. Salvatore, C., A. Cerasa, P. Battista, M. Gilardi, A. Quattrone, and I. Castiglioni, "Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: A machine learning approach," Frontiers in Neuroscience, Vol. 9, 307-319, 2015.

37. Liu, M., D. Cheng, K. Wang, et al. "Multi-modality cascaded convolutional neural networks for Alzheimer's disease diagnosis," Neuroinformatics, Vol. 16, No. 3-4, 295-308, 2018.