11 citations · 23 across the 13 of their papers we have counts for
7 papers · 1 filter
BINDER: A Latent Variable Model for Probabilistic Medical Image Registration
Stefano Cerri, Amirhossein Hassankhani, Yaël Balbastre +1
We propose a new probabilistic model for general-purpose medical image registration that builds upon the mutual information registration criterion. It centers around a spatial inte…
Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology
Kyriaki-Margarita Bintsi, Sparsh Makharia, Yaël Balbastre +4
Diffusion MRI (dMRI) tractography enables non-invasive reconstruction of white-matter pathways, but its accuracy is fundamentally limited by indirect, low-resolution measurements o…
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…
SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration
Sean I. Young, Yaël Balbastre, Adrian V. Dalca +3
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent resul…
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…
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…