60 citations · 138 across the 6 of their papers we have counts for
6 papers
TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds
Yui Lo, Yuqian Chen, Dongnan Liu +9
Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship…
DDEvENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRI
Chenjun Li, Dian Yang, Shun Yao +14
In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the des…
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…
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…
Filtering in tractography using autoencoders (FINTA)
Jon Haitz Legarreta, Laurent Petit, François Rheault +4
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlyin…