26 citations · 98 across the 19 of their papers we have counts for
19 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…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
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…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
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…
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…
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
Neil P. Oxtoby, Fabio S. Ferreira, Agoston Mihalik +12
We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions acr…
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
Agoston Mihalik, Mikael Brudfors, Maria Robu +12
We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABC…
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…
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…