Deformable Medical Image Registration
16 papers with code • 0 benchmarks • 0 datasets
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Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration
jianqingzheng/res_aligner_net
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Medical Image Analysis 2023
Deformable image registration, the estimation of the spatial transformation between different images, is an important task in medical imaging.
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
mungomeng/registration-autofuse
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However, DNN-based registration needs to explore the spatial information of each image and fuse this information to characterize spatial correspondence.
SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
honkamj/sitreg
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Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images.
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image.
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration.
Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions
Deformable image registration plays a critical role in various tasks of medical image analysis.
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.
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.
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.
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.
