26 citations · 87 across the 21 of their papers we have counts for
21 papers
Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation
Liam Chalcroft, Ruben Lourenço Pereira, Mikael Brudfors +5
Vision transformers are effective deep learning models for vision tasks, including medical image segmentation. However, they lack efficiency and translational invariance, unlike co…
Deep Variational Lesion-Deficit Mapping
Guilherme Pombo, Robert Gray, Amy P. K. Nelson +3
Causal mapping of the functional organisation of the human brain requires evidence of \textit{necessity} available at adequate scale only from pathological lesions of natural origi…
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Mikael Brudfors, Yael Balbastre, John Ashburner +4
Data used in image segmentation are not always defined on the same grid. This is particularly true for medical images, where the resolution, field-of-view and orientation can diffe…
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…
Flexible Bayesian Modelling for Nonlinear Image Registration
Mikael Brudfors, Yaël Balbastre, Guillaume Flandin +2
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. Th…
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Yaël Balbastre, Mikael Brudfors, Michela Azzarito +3
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters -- such as the apparent transverse relaxation rate R2*, the longitudinal relaxation rate R1 and th…
Groupwise Multimodal Image Registration using Joint Total Variation
Mikael Brudfors, Yaël Balbastre, John Ashburner
In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between s…
A Tool for Super-Resolving Multimodal Clinical MRI
Mikael Brudfors, Yael Balbastre, Parashkev Nachev +1
We present a tool for resolution recovery in multimodal clinical magnetic resonance imaging (MRI). Such images exhibit great variability, both biological and instrumental. This var…
Empirical Bayesian Mixture Models for Medical Image Translation
Mikael Brudfors, John Ashburner, Parashkev Nachev +1
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpre…
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Guilherme Pombo, Robert Gray, Tom Varsavsky +2
Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of v…
Nonlinear Markov Random Fields Learned via Backpropagation
Mikael Brudfors, Yaël Balbastre, John Ashburner
Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on…
MRI Super-Resolution using Multi-Channel Total Variation
Mikael Brudfors, Yael Balbastre, Parashkev Nachev +1
This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recove…
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
Mikael Agn, Per Munck af Rosenschöld, Oula Puonti +7
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. The method comb…
An Algorithm for Learning Shape and Appearance Models without Annotations
John Ashburner, Mikael Brudfors, Kevin Bronik +1
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. It is based on the idea that having a more accurate s…
Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Yaël Balbastre, Mikael Brudfors, Kevin Bronik +1
Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques s…
Generative diffeomorphic atlas construction from brain and spinal cord MRI data
Claudia Blaiotta, Patrick Freund, M. Jorge Cardoso +1
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 neur…