干旱区生态环境知识资源中心(Arid): Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain

Arid

DOI	10.2174/1573405615666181120141147
	Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain
	Alam, Saadia Binte 1; Nii, Manabu 1; Shimizu, Akinobu 2; Kobashi, Syoji 1
通讯作者	Alam, Saadia Binte
来源期刊	CURRENT MEDICAL IMAGING
ISSN	1573-4056
EISSN	1875-6603
出版年	2020
卷号	16 期号:5 页码:499-506
英文摘要	Background: This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time. Aims: Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by E-step using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject's age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter. Methods: The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean +/- SD = 59.32 +/- 16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2. Results: We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer's disease (AD) identification utilizing support vector machine. Conclusion: In this study, st-SSM based adult brain shape feature extraction and classification techniques are introduced to classify between normal and AD subject as an application.
英文关键词	Spatiotemporal statistical shape model brain magnetic resonance imaging shape analysis age Alzheimer's disease identification
类型	Article
语种	英语
国家	Japan
收录类别	SCI-E
WOS记录号	WOS:000537880500005
WOS关键词	SEGMENTATION ; CONSTRUCTION
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/318822
作者单位	1.Univ Hyogo, Grad Sch Engn, Kobe, Hyogo, Japan; 2.Tokyo Univ Agr & Technol, Tokyo, Japan
推荐引用方式 GB/T 7714	Alam, Saadia Binte,Nii, Manabu,Shimizu, Akinobu,et al. Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain[J],2020,16(5):499-506.
APA	Alam, Saadia Binte,Nii, Manabu,Shimizu, Akinobu,&Kobashi, Syoji.(2020).Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain.CURRENT MEDICAL IMAGING,16(5),499-506.
MLA	Alam, Saadia Binte,et al."Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain".CURRENT MEDICAL IMAGING 16.5(2020):499-506.