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In medical imaging, most of the image registration methods implicitly assume a one-to-one correspondence between the source and target images (i.e., diffeomorphism).
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X. Liu et al, · 2015
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B.H. Menze et al, · 2015
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K. He et al, · 2016
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“MRI atlas of IDH wild-type supratentorial glioblastoma: Probabilistic maps of phenotype, management, and outcomes,”
A. Roux et al, · 2019
Cited alongside, same era.
“Coupling brain-tumor biophysical models and diffeomorphic image registration,”
K. Scheufele et al, · 2019
Later among the works it cites.
“VoxelMorph: A Learning Framework for Deformable Medical Image Registration,”
G. Balakrishnan et al, · 2019
Later among the works it cites.
“Learning Joint Shape and Appearance Representations with Metamorphic Auto-Encoders,”
A. Bône et al, · 2020
Later among the works it cites.
“Residual Networks as Flows of Diffeomorphisms,”
F. Rousseau, L. Drumetz, and R. Fablet, · 2020
Later among the works it cites.
“Metamorphic image registration using a semi-Lagrangian scheme,”
A. François et al, · 2021
Later among the works it cites.
“ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks,”
B. Ben Amor et al, · 2021
Later among the works it cites.
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“OASIS: Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults.,”
DS. Marcus et al,
Cited in the paper.