An Integrated Method for Large Deformable Registration of Brain Images

2021 ◽  
Author(s):  
Pengcheng Yu ◽  
Yao Li
Keyword(s):  
Deformable Registration ◽  
Brain Images ◽  
Integrated Method

A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research

2007 ◽  
Vol 26 (4) ◽  
pp. 452-461 ◽  
Author(s):  
Daniel Schwarz ◽  
Tomas Kasparek ◽  
Ivo Provaznik ◽  
Jiri Jarkovsky
Keyword(s):  
Deformable Registration ◽  
Brain Images ◽  
Registration Method ◽  
Mri Brain ◽  
Automated Morphometry ◽  
Mri Brain Images

Learning-based deformable registration of MR brain images

2006 ◽  
Vol 25 (9) ◽  
pp. 1145-1157 ◽  
Author(s):  
Guorong Wu ◽  
Feihu Qi ◽  
Dinggang Shen
Keyword(s):  
Deformable Registration ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

scholarly journals Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaodan Sui ◽  
Yuanjie Zheng ◽  
Yunlong He ◽  
Weikuan Jia
Keyword(s):  
Anatomical Variations ◽  
Deformable Registration ◽  
Deformation Field ◽  
Image Pair ◽  
Imaging Analysis ◽  
Brain Images ◽  
Image Guided ◽  
Mr Brain Images ◽  
Image Pairs ◽  
Mr Brain

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.

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