Search Results for author:
Found 26 papers, 5 papers with code
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography
We propose Tract2Shape, a novel multimodal deep learning framework that leverages geometric (point cloud) and scalar (tabular) features to predict ten white matter tractography shape measures.
DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography
Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the nuclei of interest for studying its subdivisions.
A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI
Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation.
TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds
In this work, we investigate the possibility of utilizing a deep learning model to compute shape measures of the brain's white matter connections.
The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
Our results demonstrate that shape measures are predictive of individual cognitive performance.
Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation.
TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Classification from Diffusion MRI Tractography
The proposed approach highlights the potential of integrating local anatomical information and global feature dependencies to improve prediction performance in machine learning with diffusion MRI tractography.
A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations
However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data.
Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction
Overall, our results indicate that the shape of the brain's connections is predictive of human language function.
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance.
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations.
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
TractCloud achieves efficient and consistent whole-brain white matter parcellation across the lifespan (from neonates to elderly subjects, including brain tumor patients) without the need for registration.
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project.
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body.
Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypes
In this paper, we investigate the potential of fiber tract shape features for predicting non-imaging phenotypes, both individually and in combination with traditional features.
TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractography
This work shows the potential of incorporating anatomical information, especially known anatomical similarities between input features, to guide convolutions in neural networks.
Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method
In this paper, we investigate a deep nonnegative matrix factorization clustering method (DNMFC) for parcellation of the human DN based on its structural connectivity using diffusion MRI tractography.
DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive Learning Framework for Automated Identification Of The Retinogeniculate Pathway Using dMRI Tractography
The retinogeniculate pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus.
Age Prediction Performance Varies Across Deep, Superficial, and Cerebellar White Matter Connections
The brain's white matter (WM) undergoes developmental and degenerative processes during the human lifespan.
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography
We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography.
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography.
White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning
In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF).
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation
In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances.
Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach
The human brain's white matter (WM) structure is of immense interest to the scientific community.
SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning
Most parcellation methods focus on the deep white matter (DWM), while fewer methods address the superficial white matter (SWM) due to its complexity.
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.
