Deformable Medical Image Registration
16 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
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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.
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.
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.
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.
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.
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.
