干旱区生态环境知识资源中心(Arid): Construction of MRI-Based Alzheimer's Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset

Arid

DOI	10.3233/JAD-200830
	Construction of MRI-Based Alzheimer's Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset
	Yee, Evangeline; Ma, Da; Popuri, Karteek; Wang, Lei; Beg, Mirza Faisal
通讯作者	Ma, D ; Beg, MF (corresponding author), Simon Fraser Univ, ASB 8857,8888 Univ Dr, Burnaby, BC V5A 1S6, Canada.
来源期刊	JOURNAL OF ALZHEIMERS DISEASE
ISSN	1387-2877
EISSN	1875-8908
出版年	2021
卷号	79 期号:1 页码:47-58
英文摘要	Background: In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer's disease. Due to memory constraints, many of the proposed CNNs work at a 2D slice-level or 3D patch-level. Objective: Here, we propose a subject-level 3D CNN that can extract the neurodegenerative patterns of the whole brain MRI and converted into a probabilistic Dementia score. Methods: We propose an efficient and lightweight subject-level 3D CNN featuring dilated convolutions. We trained our network on the ADNI data on stable Dementia of the Alzheimer's type (sDAT) from stable normal controls (sNC). To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other sites (AIBL), images acquired using different protocols (OASIS), and longitudinal images acquired over a short period of time (MIRIAD). Results: We achieved a 5-fold cross-validated balanced accuracy of 88% in differentiating sDAT from sNC, and an overall specificity of 79.5% and sensitivity 79.7% on the entire set of 7,902 independent test images. Conclusion: Independent testing is essential for estimating the generalization ability of the network to unseen data, but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. Our comprehensive evaluation highlighting the competitive performance of our network and potential promise for generalization.
英文关键词	3D CNN dementia of Alzheimer's type (DAT) magnetic resonance imaging
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000606806900007
WOS关键词	STRUCTURAL MRI ; CLASSIFICATION ; MILD ; DEMENTIA
WOS类目	Neurosciences
WOS研究方向	Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/347975
作者单位	[Yee, Evangeline; Ma, Da; Popuri, Karteek; Beg, Mirza Faisal] Simon Fraser Univ, Sch Engn Sci, Burnaby, BC, Canada; [Wang, Lei] Northwestern Univ, Feinberg Sch Med, Chicago, IL 60611 USA
推荐引用方式 GB/T 7714	Yee, Evangeline,Ma, Da,Popuri, Karteek,et al. Construction of MRI-Based Alzheimer's Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset[J],2021,79(1):47-58.
APA	Yee, Evangeline,Ma, Da,Popuri, Karteek,Wang, Lei,&Beg, Mirza Faisal.(2021).Construction of MRI-Based Alzheimer's Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset.JOURNAL OF ALZHEIMERS DISEASE,79(1),47-58.
MLA	Yee, Evangeline,et al."Construction of MRI-Based Alzheimer's Disease Score Based on Efficient 3D Convolutional Neural Network: Comprehensive Validation on 7,902 Images from a Multi-Center Dataset".JOURNAL OF ALZHEIMERS DISEASE 79.1(2021):47-58.