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Found 10 papers, 3 papers with code
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.
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.
Learning Expected Appearances for Intraoperative Registration during Neurosurgery
In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy.
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.
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound
B-line artifacts in LUS videos are key findings associated with pulmonary congestion.
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).
