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
Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks
annareithmeir/elastic-regularization-hypermorph
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In particular, we adapt the HyperMorph framework to learn the effect of the two elasticity parameters of the linear elastic regularizer.
Robust deformable image registration using cycle-consistent implicit representations
louisvh/cycle_consistent_inr
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To improve robustness, we propose a deformable registration method using pairs of cycle-consistent Implicit Neural Representations: each implicit representation is linked to a second implicit representation that estimates the opposite transformation, causing each network to act as a regularizer for its paired opposite.
QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
louelin/quiz
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Rigid pre-registration involving local-global matching or other large deformation scenarios is crucial.
Preserving Tumor Volumes for Unsupervised Medical Image Registration
dddraxxx/Medical-Volume-Preserving-Reg
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ICCV 2023
Our approach balances image similarity and volume preservation in different regions, i. e., normal and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one.
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
mungomeng/registration-autofuse
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However, DNN-based registration needs to explore the spatial information of each image and fuse this information to characterize spatial correspondence.
On-the-Fly Guidance Training for Medical Image Registration
miraclefactory/on-the-fly-guidance
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OFG notably boosts the precision of existing image registration techniques while maintaining the speed of learning-based methods.
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies.
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
Instead of directly predicting a full-resolution displacement field, our Fourier-Net learns a low-dimensional representation of the displacement field in the band-limited Fourier domain which our model-driven decoder converts to a full-resolution displacement field in the spatial domain.
ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer
ZAX130/SmileCode
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The Transformer structures have been widely used in computer vision and have recently made an impact in the area of medical image registration.
Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration
m1nhengchen/sopi
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We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard.
