88 citations · 296 across the 16 of their papers we have counts for
7 papers · 1 filter
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering
Daniel Jörgens, Pierre-Marc Jodoin, Maxime Descoteaux +1
One of the main issues of the current tractography methods is their high false-positive rate. Tractogram filtering is an option to remove false-positive streamlines from tractograp…
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Félix Dumais, Jon Haitz Legarreta, Carl Lemaire +7
White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and a…
Generative Sampling in Bundle Tractography using Autoencoders (GESTA)
Jon Haitz Legarreta, Laurent Petit, Pierre-Marc Jodoin +1
Current tractography methods use the local orientation information to propagate streamlines from seed locations. Many such seeds provide streamlines that stop prematurely or fail t…
Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging
Benoit Anctil-Robitaille, Antoine Théberge, Pierre-Marc Jodoin +3
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to 8 times larger than tho…
Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track
Tal Arbel, Ismail Ben Ayed, Marleen de Bruijne +3
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9…
NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI
Mauro Zucchelli, Maxime Descoteaux, Gloria Menegaz
Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging technique which is able to detect the principal directions of water diffusion as well as neurites densi…
Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising
Samuel St-Jean, Pierrick Coupé, Maxime Descoteaux
Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise Ratio, especially at high b-values. Acquiring data at high b-values contains relevant information and…