Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

Yuanyuan Chen; Haozhe Jia; Zhaowei Huang; Yong Xia

doi:10.1007/978-3-030-00563-4_29

Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

Yuanyuan Chen, Haozhe Jia, Zhaowei Huang, Yong Xia

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Alzheimer’s disease (AD) has become a nonnegligible global health threat and social problem as the world population ages. The ability to identify AD subjects in an early stage will be increasingly important as disease modifying therapies for AD are developed. In this paper, we propose an ensemble of 3D convolutional neural networks (en3DCNN) for automated identification of AD patients from normal controls using structural magnetic resonance imaging (MRI). We first employ the anatomical automatic labeling (AAL) cortical parcellation map to obtain 116 cortical volumes, then use the samples extracted from each cortical volume to train a 3D convolutional neural network (CNN), and finally assemble the predictions made by well-performed 3D CNNs via majority voting to classify each subject. We evaluated our algorithm against six existing algorithms on 764 MRI scans selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our results indicate that the proposed en3DCNN algorithm is able to achieve the state-of-the-art performance in early identification of Alzheimer’s Disease using structural MRI.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings
Editors	Amir Hussain, Bin Luo, Jiangbin Zheng, Xinbo Zhao, Cheng-Lin Liu, Jinchang Ren, Huimin Zhao
Publisher	Springer Verlag
Pages	303-311
Number of pages	9
ISBN (Print)	9783030005627
DOIs	https://doi.org/10.1007/978-3-030-00563-4_29
State	Published - 2018
Event	9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 - Xi'an, China Duration: 7 Jul 2018 → 8 Jul 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10989 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018
Country/Territory	China
City	Xi'an
Period	7/07/18 → 8/07/18

Keywords

3D convolutional neural network
Alzheimer’s disease
Computer-aided diagnosis
Ensemble learning

Access to Document

10.1007/978-3-030-00563-4_29

Cite this

Chen, Y., Jia, H., Huang, Z., & Xia, Y. (2018). Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging. In A. Hussain, B. Luo, J. Zheng, X. Zhao, C.-L. Liu, J. Ren, & H. Zhao (Eds.), Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings (pp. 303-311). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10989 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-00563-4_29

Chen, Yuanyuan ; Jia, Haozhe ; Huang, Zhaowei et al. / Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging. Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings. editor / Amir Hussain ; Bin Luo ; Jiangbin Zheng ; Xinbo Zhao ; Cheng-Lin Liu ; Jinchang Ren ; Huimin Zhao. Springer Verlag, 2018. pp. 303-311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ea9e87bf28174711a6c4c3d4ec23ae21,

title = "Early Identification of Alzheimer{\textquoteright}s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging",

abstract = "Alzheimer{\textquoteright}s disease (AD) has become a nonnegligible global health threat and social problem as the world population ages. The ability to identify AD subjects in an early stage will be increasingly important as disease modifying therapies for AD are developed. In this paper, we propose an ensemble of 3D convolutional neural networks (en3DCNN) for automated identification of AD patients from normal controls using structural magnetic resonance imaging (MRI). We first employ the anatomical automatic labeling (AAL) cortical parcellation map to obtain 116 cortical volumes, then use the samples extracted from each cortical volume to train a 3D convolutional neural network (CNN), and finally assemble the predictions made by well-performed 3D CNNs via majority voting to classify each subject. We evaluated our algorithm against six existing algorithms on 764 MRI scans selected from the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI) database. Our results indicate that the proposed en3DCNN algorithm is able to achieve the state-of-the-art performance in early identification of Alzheimer{\textquoteright}s Disease using structural MRI.",

keywords = "3D convolutional neural network, Alzheimer{\textquoteright}s disease, Computer-aided diagnosis, Ensemble learning",

author = "Yuanyuan Chen and Haozhe Jia and Zhaowei Huang and Yong Xia",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 ; Conference date: 07-07-2018 Through 08-07-2018",

year = "2018",

doi = "10.1007/978-3-030-00563-4_29",

language = "英语",

isbn = "9783030005627",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "303--311",

editor = "Amir Hussain and Bin Luo and Jiangbin Zheng and Xinbo Zhao and Cheng-Lin Liu and Jinchang Ren and Huimin Zhao",

booktitle = "Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings",

}

Chen, Y, Jia, H, Huang, Z & Xia, Y 2018, Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging. in A Hussain, B Luo, J Zheng, X Zhao, C-L Liu, J Ren & H Zhao (eds), Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10989 LNAI, Springer Verlag, pp. 303-311, 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018, Xi'an, China, 7/07/18. https://doi.org/10.1007/978-3-030-00563-4_29

Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging. / Chen, Yuanyuan; Jia, Haozhe; Huang, Zhaowei et al.
Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings. ed. / Amir Hussain; Bin Luo; Jiangbin Zheng; Xinbo Zhao; Cheng-Lin Liu; Jinchang Ren; Huimin Zhao. Springer Verlag, 2018. p. 303-311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10989 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

AU - Chen, Yuanyuan

AU - Jia, Haozhe

AU - Huang, Zhaowei

AU - Xia, Yong

PY - 2018

Y1 - 2018

N2 - Alzheimer’s disease (AD) has become a nonnegligible global health threat and social problem as the world population ages. The ability to identify AD subjects in an early stage will be increasingly important as disease modifying therapies for AD are developed. In this paper, we propose an ensemble of 3D convolutional neural networks (en3DCNN) for automated identification of AD patients from normal controls using structural magnetic resonance imaging (MRI). We first employ the anatomical automatic labeling (AAL) cortical parcellation map to obtain 116 cortical volumes, then use the samples extracted from each cortical volume to train a 3D convolutional neural network (CNN), and finally assemble the predictions made by well-performed 3D CNNs via majority voting to classify each subject. We evaluated our algorithm against six existing algorithms on 764 MRI scans selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our results indicate that the proposed en3DCNN algorithm is able to achieve the state-of-the-art performance in early identification of Alzheimer’s Disease using structural MRI.

AB - Alzheimer’s disease (AD) has become a nonnegligible global health threat and social problem as the world population ages. The ability to identify AD subjects in an early stage will be increasingly important as disease modifying therapies for AD are developed. In this paper, we propose an ensemble of 3D convolutional neural networks (en3DCNN) for automated identification of AD patients from normal controls using structural magnetic resonance imaging (MRI). We first employ the anatomical automatic labeling (AAL) cortical parcellation map to obtain 116 cortical volumes, then use the samples extracted from each cortical volume to train a 3D convolutional neural network (CNN), and finally assemble the predictions made by well-performed 3D CNNs via majority voting to classify each subject. We evaluated our algorithm against six existing algorithms on 764 MRI scans selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our results indicate that the proposed en3DCNN algorithm is able to achieve the state-of-the-art performance in early identification of Alzheimer’s Disease using structural MRI.

KW - 3D convolutional neural network

KW - Alzheimer’s disease

KW - Computer-aided diagnosis

KW - Ensemble learning

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DO - 10.1007/978-3-030-00563-4_29

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SN - 9783030005627

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings

A2 - Hussain, Amir

A2 - Luo, Bin

A2 - Zheng, Jiangbin

A2 - Zhao, Xinbo

A2 - Liu, Cheng-Lin

A2 - Ren, Jinchang

A2 - Zhao, Huimin

PB - Springer Verlag

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Chen Y, Jia H, Huang Z, Xia Y. Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging. In Hussain A, Luo B, Zheng J, Zhao X, Liu CL, Ren J, Zhao H, editors, Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings. Springer Verlag. 2018. p. 303-311. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00563-4_29

Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

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