Diffeomorphic Medical Image Registration
6 papers with code • 3 benchmarks • 2 datasets
Diffeomorphic mapping is the underlying technology for mapping and analyzing information measured in human anatomical coordinate systems which have been measured via Medical imaging. Diffeomorphic mapping is a broad term that actually refers to a number of different algorithms, processes, and methods. It is attached to many operations and has many applications for analysis and visualization. Diffeomorphic mapping can be used to relate various sources of information which are indexed as a function of spatial position as the key index variable. Diffeomorphisms are by their Latin root structure preserving transformations, which are in turn differentiable and therefore smooth, allowing for the calculation of metric based quantities such as arc length and surface areas. Spatial location and extents in human anatomical coordinate systems can be recorded via a variety of Medical imaging modalities, generally termed multi-modal medical imagery, providing either scalar and or vector quantities at each spatial location.
( Image credit: Quicksilver )
Benchmarks
These leaderboards are used to track progress in Diffeomorphic Medical Image Registration
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Libraries
Use these libraries to find Diffeomorphic Medical Image Registration models and implementations
PPMI
ACDC
Latest papers
Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis
SCAN-NRAD/DL-DiReCT
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Human Brain Mapping 2022
The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image.
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation
DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness.
Metric Learning for Image Registration
uncbiag/registration
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CVPR 2019
Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
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).
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling.
