Deformable Medical Image Registration
18 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Deformable Medical Image Registration models and implementationsMost implemented papers
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
An Unsupervised Learning Model for Deformable Medical Image Registration
balakg/voxelmorph
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CVPR 2018
We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
Deformable medical image registration: setting the state of the art with discrete methods
To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem.
Metric Learning for Image Registration
uncbiag/registration
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CVPR 2019
Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
ToFec/OneShotImageRegistration
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In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets.
Learning Conditional Deformable Templates with Convolutional Networks
voxelmorph/voxelmorph
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NeurIPS 2019
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks.
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021
multimodallearning/convexadam
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Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations.
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
Algolzw/SPAC-Deformable-Registration
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We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
Coordinate Translator for Learning Deformable Medical Image Registration
yihao6/digital_diffeomorphism
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The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.
