3260 papers • 126 benchmarks • 313 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 )
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VoxelMorph promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications and demonstrates that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster.
This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
A probabilistic generative model is presented and an unsupervised learning-based inference algorithm is derived that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
This work embeds a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself, allowing controlling the desired level of regularity and preserving structural properties of a registration model.
DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness, and experiments suggest that both DiReCT‐based methods had higher sensitivity to changes in cortex thickness than Freesurfer.
It is hypothesized that deep learning (DL)‐based morphometry methods can extract morphometric measures also from contrast‐enhanced MRI, making additional cases available for analysis and potential future diagnostic morphometry tools.
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