Unsupervised Image Registration
10 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Image Registration
Most implemented papers
DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
Specifically, in a one-shot-scenario (with only one manually labeled image) our approach increases Dice scores (%) over an unsupervised registration network by 2. 7 and 1. 8 on the knee and brain images respectively.
DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration
In this work, we present a novel unsupervised image registration algorithm.
FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow
The photometric loss minimizes pixel intensity differences differences, the smoothness loss encourages similar magnitudes between neighbouring vectors, and a correlation loss that is used to maintain the intensity similarity between fixed and moving image slices.
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor
riddhishb/self-supervised-landmarks
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We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well.
Symmetric Transformer-based Network for Unsupervised Image Registration
Medical image registration is a fundamental and critical task in medical image analysis.
Fourier-Net: Fast Image Registration with Band-limited Deformation
Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain.
RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration
Furthermore, shifted window partitioning operations are inflexible, indicating that they cannot perceive the semantic information over uncertain distances and automatically bridge the global connections between windows.
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
Instead of directly predicting a full-resolution displacement field, our Fourier-Net learns a low-dimensional representation of the displacement field in the band-limited Fourier domain which our model-driven decoder converts to a full-resolution displacement field in the spatial domain.
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.
High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D CINE MRI and Unsupervised Neural Networks
We create maps of LA Displacement Vector Field (DVF) magnitude and LA principal strain values from images of 10 healthy volunteers and 8 patients with cardiovascular disease (CVD).
