26 citations · 98 across the 19 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…