Deformable Medical Image Registration
15 papers with code • 0 benchmarks • 0 datasets
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HNAS-reg: hierarchical neural architecture search for deformable medical image registration
Convolutional neural networks (CNNs) have been widely used to build deep learning models for medical image registration, but manually designed network architectures are not necessarily optimal.
Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
We propose a novel framework for deformable image registration.
A Transformer-based Network for Deformable Medical Image Registration
Deformable medical image registration plays an important role in clinical diagnosis and treatment.
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution.
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation
We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images.
Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior
Our approach uses the idea of deep image priors to combine convolutional networks with conventional registration methods based on manually engineered priors.
Deformable Registration Using Average Geometric Transformations for Brain MR Images
Accurate registration of medical images is vital for doctor's diagnosis and quantitative analysis.
Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer’s disease
We overcome this challenge by developing a new registration technique that simultaneously classifies each pixel as “good data” / “missing tissue” / “artifact”, learns a contrast transformation between modalities, and computes deformation parameters.
Learning a Probabilistic Model for Diffeomorphic Registration
Besides, we visualized the learned latent space and show that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
Unsupervised End-to-end Learning for Deformable Medical Image Registration
The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems.