Medical Image Registration
77 papers with code • 4 benchmarks • 8 datasets
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
Benchmarks
These leaderboards are used to track progress in Medical Image Registration
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Libraries
Use these libraries to find Medical Image Registration models and implementationsDatasets
PPMI
Learn2Reg
OASIS
IXI
SR-Reg
Full-Spectral Autofluorescence Lifetime Microscopic Images
BreastDICOM4
Latest papers with no code
Particle Swarm Optimization in 3D Medical Image Registration: A Systematic Review
Medical image registration seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures.
SETGen: Scalable and Efficient Template Generation Framework for Groupwise Medical Image Registration
Secondly, we explore a siamese training scheme that feeds two images to the shared-weight twin networks and compares the distances between inputs and the generated template to prompt the template to be close to the implicit center.
Meta-Learning Initializations for Interactive Medical Image Registration
We present a meta-learning framework for interactive medical image registration.
ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross- and Self-Attention Fusion
Deep learning-based methods have been studied for medical image registration, which leverage convolutional neural networks (CNNs) for efficiently regressing a dense deformation field from a pair of images.
Medical image registration using unsupervised deep neural network: A scoping literature review
In medicine, image registration is vital in image-guided interventions and other clinical applications.
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration
Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes.
Medical Image Registration via Neural Fields
Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images.
Deep Learning for Medical Image Registration: A Comprehensive Review
Finally, a discussion is provided on the promising future research areas in the field of DL-based medical image registration.
PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation
The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain.
Region Specific Optimization (RSO)-based Deep Interactive Registration
A test time optimization (TTO) technique was proposed to further improve the DL models' performance.
