Search Results for author:
Found 8 papers, 6 papers with code
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid.
Factorisation-based Image Labelling
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging.
Model-based multi-parameter mapping
Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e. g., maximum likelihood or maximum a posteriori).
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).
A Tool for Super-Resolving Multimodal Clinical MRI
The model-driven nature of the approach means that no type of training is needed for applicability to the diversity of MR contrasts present in a clinical context.
Empirical Bayesian Mixture Models for Medical Image Translation
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications.
MRI Super-Resolution using Multi-Channel Total Variation
This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast.
An Algorithm for Learning Shape and Appearance Models without Annotations
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images.
