Early Detection of Alzheimer’s Disease by Segmenting Hippocampus from MRI of Human Brain Using Deep Learning

Authors

  • D. K. Ramkumar Research Scholar, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India
  • N. V. Balaji 2Professor, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India.

Keywords:

Convolution Neural Network, hippocampus, Artificial intelligence, segmentation, brain diseases

Abstract

To investigate a number of neurodegenerative disorders, including Alzheimer's disease, an automatic measurement of hippocampal volume extraction is essential. It is particularly important to examine the features of the hippocampus subfields since they can reveal earlier disease proliferation in the human brain. Due to their complicated structural structure and the requirement for manually labelled high-resolution magnetic resonance images (MRI), segmentation of these subfields is extremely challenging. In the presented paper, we introduce a thoroughly supervised convolutional neural network-based model called VNet for autonomous hippocampal subfield segmentation. The experiments carried out on the challenging data set and their qualitative and quantitative results are reported here. The method has provided improved results in the measures of accuracy and dice score when compared to other cutting-edge methods.

Downloads

Download data is not yet available.

References

Y. Mu, and F.H. Gage, 2011. Adult hippocampal neurogenesis and its role in Alzheimer's disease. Molecular neurodegeneration, 6(1), pp.1-9.

Y. Fan W. Huang Z. Lin W. Zhu J. Zhou, and J. Wong, 2015. Brain tumor grading based on neural networks and convolutional neural network, 37th International Conference of IEEE Eng. in Medicine & Biology Society, pp. 699-702.

L. Zhao, and K. Jia, 2016. Multiscale CNNs for brain tumor segmentation and diagnosis. Computational and mathematical methods in medicine, 2016.

Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V.C. Mok, L. Shi, and P.A. Heng, 2016. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE transactions on medical imaging, 35(5), pp.1182-1195.

J.E. Iglesias, J.C. Augustinack, K. Nguyen, C.M. Player, A. Player, M. Wright, N. Roy, M.P. Frosch, A.C. McKee, L.L. Wald, and B. Fischl, 2015. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. Neuroimage, 115, pp.117-137.

P.A. Yushkevich, J.B. Pluta, H. Wang, L. Xie, S.L. Ding, E.C. Gertje, L. Mancuso, D. Kliot, S.R. Das, and D.A. Wolk, 2015. Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Human brain mapping, 36(1), pp.258-287.

M. Goubran, E.E. Ntiri, H. Akhavein, M. Holmes, S. Nestor, J. Ramirez, S. Adamo, M. Ozzoude, C. Scott, F. Gao, and A. Martel, 2020. Hippocampal segmentation for brains with extensive atrophy using three‐dimensional convolutional neural networks (Vol. 41, No. 2, pp. 291-308). Hoboken, USA: John Wiley & Sons, Inc..

M. Liu, F. Li, H. Yan, K. Wang, Y. Ma, L. Shen, M. Xu, and Alzheimer’s Disease Neuroimaging Initiative, 2020. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage, 208, p.116459.

Z. Yang, X. Zhuang, V. Mishra, K. Sreenivasan, and D. Cordes, 2020. CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox. NeuroImage, 218, p.116947.

Y. Chen, B. Shi, Z. Wang, P. Zhang, C.D. Smith,. and J. Liu, 2017, April. Hippocampus segmentation through multi-view ensemble ConvNets. In 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017) (pp. 192-196). IEEE.

Z. Xie, and D. Gillies, 2018. Near real-time hippocampus segmentation using patch-based canonical neural network. arXiv preprint arXiv:1807.05482.

C. Wachinger, M. Reuter, and T. Klein, 2018. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy. NeuroImage, 170, pp.434-445.

A.G. Roy, S. Conjeti, N. Navab, C. Wachinger, and Alzheimer's Disease Neuroimaging Initiative, 2019. QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage, 186, pp.713-727.

F. Isensee, J. Petersen, A. Klein, D. Zimmerer, P.F. Jaeger, S. Kohl, J. Wasserthal, G. Koehler, T. Norajitra, S.Wirkert, and K.H. Maier-Hein, 2018. nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486.

M. Perslev, E.B. Dam, A. Pai, and C. Igel, 2019. One network to segment them all: A general, lightweight system for accurate 3d medical image segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22 (pp. 30-38). Springer International Publishing.

Y. Xia, D. Yang, Z. Yu, F. Liu, J. Cai, L. Yu, Z. Zhu, D. Xu, A. Yuille, and H. Roth, 2020. Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Medical image analysis, 65, p.101766.

Q. Yu, D. Yang, H. Roth, Y. Bai, Y. Zhang, A.L. Yuille, and D. Xu, 2020. C2fnas: Coarse-to-fine neural architecture search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4126-4135).

Dr. Bhushan Bandre. (2013). Design and Analysis of Low Power Energy Efficient Braun Multiplier. International Journal of New Practices in Management and Engineering, 2(01), 08 - 16. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/12

Chinthamu, N. ., Gooda, S. K. ., Venkatachalam, C. ., S., S. ., & Malathy, G. . (2023). IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 68–76. https://doi.org/10.17762/ijritcc.v11i4s.6308

Downloads

Published

21.09.2023

How to Cite

Ramkumar, D. K. ., & Balaji, N. V. . (2023). Early Detection of Alzheimer’s Disease by Segmenting Hippocampus from MRI of Human Brain Using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 871–879. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3622

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.