ArticleMedical image analysis2023
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.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of automated methods to perform white matter tract segmentation.Frontiers in neuroscience · 2024Pooled it
- Benchmarking Generalizability in Deep Learning-Based White Matter Tract Segmentation.bioRxiv : the preprint server for biology · 2026Article
- A high-resolution functional network-organized atlas of human superficial white matter from ultra-high-field diffusion MRI.iScience · 2026Article
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Cross-population white matter atlas creation for concurrent mapping of brain connections in neonates and adults with diffusion MRI tractography.Fundamental research · 2026Article
- A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography.Human brain mapping · 2025Article
- The superficial white matter in language processing: Broca's area connections are bilaterally associated with individual performance in children and adults.bioRxiv : the preprint server for biology · 2025Article
- A review on learning-based algorithms for tractography and human brain white matter tracts recognition.Neuroradiology · 2025Review
- TractCloud-FOV: Deep Learning-Based Robust Tractography Parcellation in Diffusion MRI With Incomplete Field of View.Human brain mapping · 2025Article
- Tractography-Based Automated Identification of Retinogeniculate Visual Pathway With Novel Microstructure-Informed Supervised Contrastive Learning.Human brain mapping · 2024Article
- Morphological similarity and white matter structural mapping of new daily persistent headache: a structural connectivity and tract-specific study.The journal of headache and pain · 2024Article
- Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI.Human brain mapping · 2024Article
- Surface-based and Shape-informed U-fiber Atlasing for Robust Superficial White Matter Connectivity Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations.Scientific data · 2024Article
- TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.Medical image analysis · 2024Article
- White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.Quantitative imaging in medicine and surgery · 2024Article
- Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter.Frontiers in neuroscience · 2024Article
- TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.Frontiers in neuroscience · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Multimodal Neuroimaging Computing: Basics and Applications in Neurosurgery.Advances in experimental medicine and biology · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. 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. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.