ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks

In deformable registration, the geometric framework -- large deformation diffeomorphic metric mapping (or LDDMM, in short) -- has inspired numerous techniques for comparing, deforming, averaging and analyzing shapes or images. In this work, we make use of deep residual neural networks to solve the n...

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Bibliographic Details
Main Authors:	Arguillere, S. (Author), Ben Amor, B. (Author), Shao, L. (Author)
Format:	Article
Language:	English
Published:	IEEE Computer Society 2022
Subjects:	Computational anatomy Computational Anatomy Computational geometry Deep neural networks Deep residual neural network Deep Residual Neural Networks Deformable registration Deformation Diffeomorphic Registration Diffeomorphic registrations Geometric framework Large deformation diffeomorphic metric mappings LDDMM Neural-networks Polytopes Riemannian geometry Riemannian Geometry Topology Vector spaces Velocity
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ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks

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