26 citations · 87 across the 21 of their papers we have counts for
6 papers · 1 filter
Factorisation-based Image Labelling
Yu Yan, Yael Balbastre, Mikael Brudfors +1
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully…
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
Guilherme Pombo, Robert Gray, Jorge Cardoso +4
We describe Countersynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The mode…
Correcting inter-scan motion artefacts in quantitative R1 mapping at 7T
Yaël Balbastre, Ali Aghaeifar, Nadège Corbin +3
Purpose: Inter-scan motion is a substantial source of error in estimation, and can be expected to increase at 7T where fields are more inhomogeneous. The established co…
An MRF-UNet Product of Experts for Image Segmentation
Mikael Brudfors, Yaël Balbastre, John Ashburner +4
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-distribu…
A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets
Fabio S. Ferreira, Agoston Mihalik, Rick A. Adams +2
Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modaliti…
Model-based multi-parameter mapping
Yael Balbastre, Mikael Brudfors, Michela Azzarito +3
Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encodin…