干旱区生态环境知识资源中心(Arid): Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning in the First Year of Life

Arid

DOI	10.1109/ACCESS.2019.2957233
	Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning in the First Year of Life
	Hu, Shunbo 1; Zhang, Lintao ; Li, Guoqiang ; Liu, Mingtao ; Fu, Deqian ; Zhang, Wenyin
通讯作者	Hu, Shunbo
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2020
卷号	8 页码:25691-25705
英文摘要	Accurate medical image registration is highly important for the quantitative analysis of infant brain dynamic development in the first year of life. However, the deformable registration of infant brain magnetic resonance (MR) images is highly challenging for the following two reasons: First, there are very large anatomical and appearance variations in these longitudinal images; Second, there is a one-to-many correspondence in appearance between global anatomical tissues and the small local tissues therein. In this paper, we use a CNN (convolution neural network)-based global-and-local-label-driven deformable registration scheme. Two to-be-registered image patches are input into the UNet-style regression network. Then, a dense displacement field (DDF) between them is obtained by optimizing the total loss function between two corresponding label patches. Global and local label patches are used only during training. During inference, two new MR images are divided into many patch pairs and fed into the trained network. By averaging the deformation of the patches at the same location, the final 3D DDF between the two whole images is obtained. The highlight is that the global (white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF)) and local tissues can be registered simultaneously without any prior ground-truth deformation. Especially for the local hippocampal tissues, the Dice ratios are substantially improved after registration via our method. Experimental results are presented on the intrasubject and intersubject registration of infant brain MR images between different time points, and the intersubject registration of brain T1-weighted MR images on the OASIS-1 dataset, according to which the proposed method realizes higher accuracy on both global and local tissues compared with state-of-the-art registration methods.
英文关键词	Strain Image registration Three-dimensional displays Training Hippocampus Image segmentation Task analysis Infant brain MR images deformable registration label-driven learning
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000524659900002
WOS关键词	IMAGE REGISTRATION ; SEGMENTATION ; MRI
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314706
作者单位	1.Linyi Univ, Sch Informat Sci & Engn, Linyi 27600, Shandong, Peoples R China; 2.Linyi Univ, Linda Inst, Shandong Prov Key Lab Network Based Intelligent C, Linyi 276000, Shandong, Peoples R China
推荐引用方式 GB/T 7714	Hu, Shunbo,Zhang, Lintao,Li, Guoqiang,et al. Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning in the First Year of Life[J],2020,8:25691-25705.
APA	Hu, Shunbo,Zhang, Lintao,Li, Guoqiang,Liu, Mingtao,Fu, Deqian,&Zhang, Wenyin.(2020).Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning in the First Year of Life.IEEE ACCESS,8,25691-25705.
MLA	Hu, Shunbo,et al."Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning in the First Year of Life".IEEE ACCESS 8(2020):25691-25705.