Image Registration
236 papers with code • 5 benchmarks • 11 datasets
Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.
Source: Image registration | Wikipedia
( Image credit: Kornia )
Benchmarks
These leaderboards are used to track progress in Image Registration
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Libraries
Use these libraries to find Image Registration models and implementationsDatasets
Medical Segmentation Decathlon
LLVIP
Learn2Reg
SegTHOR
UDIS-D
FIRE
DIR-LAB COPDgene
ALTO
Fetoscopy Placenta Data
FetReg: Largescale Multi-centre Fetoscopy Placenta Dataset
Latest papers
VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module
This novel hybrid VMamba-CNN network is designed specifically for 3D image registration.
FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Registration
We demonstrate compelling improvements on image registration across a spectrum of modalities and anatomies by measuring structural and landmark overlap of the registered image volumes.
ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration
ZAX130/SmileCode
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Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions.
uniGradICON: A Foundation Model for Medical Image Registration
We therefore propose uniGradICON, a first step toward a foundation model for registration providing 1) great performance \emph{across} multiple datasets which is not feasible for current learning-based registration methods, 2) zero-shot capabilities for new registration tasks suitable for different acquisitions, anatomical regions, and modalities compared to the training dataset, and 3) a strong initialization for finetuning on out-of-distribution registration tasks.
HyperPredict: Estimating Hyperparameter Effects for Instance-Specific Regularization in Deformable Image Registration
Our approach which we call HyperPredict, implements a Multi-Layer Perceptron that learns the effect of selecting particular hyperparameters for registering an image pair by predicting the resulting segmentation overlap and measure of deformation smoothness.
Quantifying the Resolution of a Template after Image Registration
Stochastik-TU-Ilmenau/image-template-resolution
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In many image processing applications (e. g. computational anatomy) a groupwise registration is performed on a sample of images and a template image is simultaneously generated.
Pyramid Attention Network for Medical Image Registration
juliuswang-7/pan
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The advent of deep-learning-based registration networks has addressed the time-consuming challenge in traditional iterative methods. However, the potential of current registration networks for comprehensively capturing spatial relationships has not been fully explored, leading to inadequate performance in large-deformation image registration. The pure convolutional neural networks (CNNs) neglect feature enhancement, while current Transformer-based networks are susceptible to information redundancy. To alleviate these issues, we propose a pyramid attention network (PAN) for deformable medical image registration. Specifically, the proposed PAN incorporates a dual-stream pyramid encoder with channel-wise attention to boost the feature representation. Moreover, a multi-head local attention Transformer is introduced as decoder to analyze motion patterns and generate deformation fields. Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets and one abdominal MRI dataset demonstrate that our method achieves favorable registration performance, while outperforming several CNN-based and Transformer-based registration networks. Our code is publicly available at https://github. com/JuliusWang-7/PAN.
Diffeomorphic Measure Matching with Kernels for Generative Modeling
tadsgroup/kernelodetransport
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This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs), inspired by ideas from diffeomorphic matching and image registration.
Decoder-Only Image Registration
xi-jia/lessnet
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For this, we propose a novel network architecture, termed LessNet in this paper, which contains only a learnable decoder, while entirely omitting the utilization of a learnable encoder.
Local Feature Matching Using Deep Learning: A Survey
The objective of this endeavor is to furnish a comprehensive overview of local feature matching methods.
