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Medical Image Registration

Image registration, also known as image fusion or image matching, is the process of aligning two or more images based on image appearances. Medical Image Registration seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. Medical Image Registration is used in many clinical applications such as image guidance, motion tracking, segmentation, dose accumulation, image reconstruction and so on. Medical Image Registration is a broad topic which can be grouped from various perspectives. From input image point of view, registration methods can be divided into unimodal, multimodal, interpatient, intra-patient (e.g. same- or different-day) registration. From deformation model point of view, registration methods can be divided in to rigid, affine and deformable methods. From region of interest (ROI) perspective, registration methods can be grouped according to anatomical sites such as brain, lung registration and so on. From image pair dimension perspective, registration methods can be divided into 3D to 3D, 3D to 2D and 2D to 2D/3D.

Source: Deep Learning in Medical Image Registration: A Review

Papers

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Showing 151–175 of 198 papers

TitleStatusHype
A Multiple Decoder CNN for Inverse Consistent 3D Image Registration—0
Medical Image Registration Using Deep Neural Networks: A Comprehensive Review—0
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning—0
BIRL: Benchmark on Image Registration methods with Landmark validationCode0
Deep Learning in Medical Image Registration: A Review—0
Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural NetworksCode0
Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration—0
Dual-Stream Pyramid Registration NetworkCode0
Analysis of Generalized Entropies in Mutual Information Medical Image Registration—0
KLDivNet: An unsupervised neural network for multi-modality image registration—0
Zero Shot Learning for Multi-Modal Real Time Image Registration—0
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation—0
Learning Conditional Deformable Templates with Convolutional NetworksCode0
Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior—0
Recursive Cascaded Networks for Unsupervised Medical Image RegistrationCode0
Deformable Registration Using Average Geometric Transformations for Brain MR Images—0
One Shot Learning for Deformable Medical Image Registration and Periodic Motion TrackingCode0
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN—0
Dense Deformation Network for High Resolution Tissue Cleared Image Registration—0
Region-specific Diffeomorphic Metric MappingCode0
Quantitative Error Prediction of Medical Image Registration using Regression ForestsCode0
Networks for Joint Affine and Non-parametric Image RegistrationCode0
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and SurfacesCode0
Deep Learning in Medical Image Registration: A Survey—0
Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening NetworkCode0
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All datasetsIXIOASISOASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSPSR-Reg

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LL_NetDSC0.77—Unverified
2OFG + TransMorphDSC0.76—Unverified
3TransMorphDSC0.74—Unverified
4OFG + ViT-V-NetDSC0.74—Unverified
5OFG + VoxelMorphDSC0.74—Unverified
6EfficientMorphDSC0.73—Unverified
7ViT-V-NetDSC0.72—Unverified
8VoxelMorphDSC0.71—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientMorphval dsc86.7—Unverified
2Fourier-Netval dsc84.7—Unverified
3TransMorphDSC0.82—Unverified
4OFG + TransMorphDSC0.82—Unverified
5OFG + ViT-V-NetDSC0.81—Unverified
6OFG + VoxelMorphDSC0.79—Unverified
7ViT-V-NetDSC0.79—Unverified
8VoxelMorphDSC0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1VoxelMorphDice Score76.3—Unverified
#ModelMetricClaimedVerifiedStatus
1MambaMorphDice (Average)82.71—Unverified