Medical Image Registration
79 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
Quantitative Error Prediction of Medical Image Registration using Regression Forests
This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans.
Region-specific Diffeomorphic Metric Mapping
uncbiag/registration
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NeurIPS 2019
We introduce a region-specific diffeomorphic metric mapping (RDMM) registration approach.
One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
ToFec/OneShotImageRegistration
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In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets.
Learning Conditional Deformable Templates with Convolutional Networks
voxelmorph/voxelmorph
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NeurIPS 2019
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks.
Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural Networks
junyuchen245/Fully_Unsupervised_CNN_Registration_Keras
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In this work, we present a novel image registration method for creating highly anatomically detailed anthropomorphic phantoms from a single digital phantom.
BIRL: Benchmark on Image Registration methods with Landmark validation
This report presents a generic image registration benchmark with automatic evaluation using landmark annotations.
DeepFLASH: An Efficient Network for Learning-based Medical Image Registration
This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration.
DeepReg: a deep learning toolkit for medical image registration
DeepReg (https://github. com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
Registration of serial sections: An evaluation method based on distortions of the ground truths
Registration of histological serial sections is a challenging task.
FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow
The photometric loss minimizes pixel intensity differences differences, the smoothness loss encourages similar magnitudes between neighbouring vectors, and a correlation loss that is used to maintain the intensity similarity between fixed and moving image slices.
