Deep learning methods for identification of white matter Fiber tracts: review of state-of-the-art and future prospective
Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging
(dMRI) data is of great significance in health and disease. For example, analysis of fiber …
(dMRI) data is of great significance in health and disease. For example, analysis of fiber …
Diffusion mri with machine learning
D Karimi, SK Warfield - Imaging Neuroscience, 2024 - direct.mit.edu
Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique
capabilities including noninvasive probing of tissue microstructure and structural …
capabilities including noninvasive probing of tissue microstructure and structural …
TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography
TractoInferno is the world's largest open-source multi-site tractography database, including
both research-and clinical-like human acquisitions, aimed specifically at machine learning …
both research-and clinical-like human acquisitions, aimed specifically at machine learning …
Deep fiber clustering: anatomically informed unsupervised deep learning for fast and effective white matter parcellation
White matter fiber clustering (WMFC) enables parcellation of white matter tractography for
applications such as disease classification and anatomical tract segmentation. However, the …
applications such as disease classification and anatomical tract segmentation. However, the …
[HTML][HTML] FIESTA: Autoencoders for accurate fiber segmentation in tractography
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 …
brain's structural connectivity in domains such as neurological disorders, neurosurgery, and …
[HTML][HTML] Evaluation of tractogram filtering methods using human-like connectome phantoms
Tractography algorithms are prone to reconstructing spurious connections. The set of
streamlines generated with tractography can be post-processed to retain the streamlines …
streamlines generated with tractography can be post-processed to retain the streamlines …
[HTML][HTML] Deep fiber clustering: anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation
White matter fiber clustering is an important strategy for white matter parcellation, which
enables quantitative analysis of brain connections in health and disease. In combination …
enables quantitative analysis of brain connections in health and disease. In combination …
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to
enable quantification and visualization for clinical and scientific applications. Current …
enable quantification and visualization for clinical and scientific applications. Current …
Generative sampling in bundle tractography using autoencoders (GESTA)
Current tractography methods use the local orientation information to propagate streamlines
from seed locations. Many such seeds provide streamlines that stop prematurely or fail to …
from seed locations. Many such seeds provide streamlines that stop prematurely or fail to …
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering
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 tractography data …
Tractogram filtering is an option to remove false-positive streamlines from tractography data …