1 citations · 1 across the 4 of their papers we have counts for
8 papers
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…
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…
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre +6
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently hav…