26 citations · 32 across the 7 of their papers we have counts for
7 papers
Learn2Synth: Learning Optimal Data Synthesis Using Hypergradients for Brain Image Segmentation
Xiaoling Hu, Xiangrui Zeng, Oula Puonti +3
Domain randomization through synthesis is a powerful strategy to train networks that are unbiased with respect to the domain of the input images. Randomization allows networks to s…
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…
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…