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
Most implemented papers
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis.
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
uncbiag/ICON
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We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Capturing voxel-wise spatial correspondence across distinct modalities is crucial for medical image analysis.
ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration
zax130/modetv2
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Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions.
Deformable medical image registration: setting the state of the art with discrete methods
To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem.
Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields
Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction.
An Artificial Agent for Robust Image Registration
nicolas1805961/GE_Reinforcement_Learning_Image_Registration
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The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy).
Benchmarking of image registration methods for differently stained histological slides
Image registration is a common task for many biomedical analysis applications.
FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration
dykuang/Medical-image-registration
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We found that FAIM is able to maintain both the advantages of higher accuracy and fewer "folding" locations over VoxelMorph, over a range of hyper-parameters (with the same values used for both networks).
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
