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Found 23 papers, 15 papers with code
Synthetic Data for Robust Stroke Segmentation
Deep learning-based semantic segmentation in neuroimaging currently requires high-resolution scans and extensive annotated datasets, posing significant barriers to clinical applicability.
Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation
Vision transformers are effective deep learning models for vision tasks, including medical image segmentation.
Deep Variational Lesion-Deficit Mapping
Here we initiate the application of deep generative neural network architectures to the task of lesion-deficit inference, formulating it as the estimation of an expressive hierarchical model of the joint lesion and deficit distributions conditioned on a latent neural substrate.
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid.
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.
Factorisation-based Image Labelling
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging.
Correcting inter-scan motion artefacts in quantitative R1 mapping at 7T
Conclusion: The proposed methods simplify inter-scan motion correction of $R_1$ maps and are applicable at both 3T and 7T, where a body coil is typically not available.
An MRF-UNet Product of Experts for Image Segmentation
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribution data.
A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets
In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets.
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).
Flexible Bayesian Modelling for Nonlinear Image Registration
We describe a diffeomorphic registration algorithm that allows groups of images to be accurately aligned to a common space, which we intend to incorporate into the SPM software.
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters -- such as the apparent transverse relaxation rate R2*, the longitudinal relaxation rate R1 and the magnetisation transfer saturation -- that can be compared across sites and scanners and carry important information about the underlying microstructure.
Groupwise Multimodal Image Registration using Joint Total Variation
In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.
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.
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low.
Nonlinear Markov Random Fields Learned via Backpropagation
Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on mixture models are still used in practice to parcellate brains.
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.
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas.
Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques such as principal component analysis.
Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction
In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations.
Generative diffeomorphic atlas construction from brain and spinal cord MRI data
In this paper we will focus on the potential and on the challenges associated with the development of an integrated brain and spinal cord modelling framework for processing MR neuroimaging data.
