Deep learning methods for identification of white matter Fiber tracts: review of state-of-the-art and future prospective

N Ghazi, MH Aarabi, H Soltanian-Zadeh - Neuroinformatics, 2023 - Springer
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 …

Learning optimal white matter tract representations from tractography using a deep generative model for population analyses

Y Feng, BQ Chandio, T Chattopadhyay… - 18th International …, 2023 - spiedigitallibrary.org
Whole brain tractography is commonly used to study the brain's white matter fiber pathways,
but the large number of streamlines generated-up to one million per brain-can be …

StreamNet: A WAE for White Matter Streamline Analysis

A Lizarraga, KL Narr, KA Donals… - … Deep Learning in …, 2022 - proceedings.mlr.press
We present StreamNet, an autoencoder architecture for the analysis of the highly
heterogeneous geometry of large collections of white matter streamlines. This proposed …

Differentiable VQ-VAE's for Robust White Matter Streamline Encodings

A Lizarraga, B Taraku, E Honig, YN Wu… - 2024 IEEE …, 2024 - ieeexplore.ieee.org
Given the complex geometry of white matter streamlines, autoencoders have been proposed
as a dimension-reduction tool to simplify the analysis streamlines in low-dimensional latent …

Deep generative model for learning tractography streamline embeddings based on convolutional variational autoencoder

Y Feng, BQ Chandio, T Chattopadhyay… - … Society for Magnetic …, 2022 - archive.ismrm.org
We present a deep generative model to autoencode tractography streamlines into a smooth
low dimensional latent distribution, which captures their spatial and sequential information …

Learning White Matter Streamline Representations Using Transformer-based Siamese Networks with Triplet Margin Loss

S Zhong, Z Chen, G Egan - archive.ismrm.org
Robust latent representation of white matter streamlines are critical for parcellating
streamlines. This work introduced a novel transformer-based siamese network with triplet …