ArticleMedical image analysis2020
Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 111 citations in OpenAlex.
- A systematic review of automated methods to perform white matter tract segmentation.Frontiers in neuroscience · 2024Pooled it
- The role of white matter microstructure in the association between childhood maltreatment and adult post-traumatic symptoms.Molecular psychiatry · 2026Article
- Benchmarking Generalizability in Deep Learning-Based White Matter Tract Segmentation.bioRxiv : the preprint server for biology · 2026Article
- Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc.bioRxiv : the preprint server for biology · 2026Article
- ESM-AnatTractNet: Advanced deep learning model of true positive eloquent white matter tractography to improve preoperative evaluation of pediatric epilepsy surgery.Medical image analysis · 2026Article
- Cross-population white matter atlas creation for concurrent mapping of brain connections in neonates and adults with diffusion MRI tractography.Fundamental research · 2026Article
- Probabilistic mapping and automated segmentation of human brainstem white matter bundles.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- 3D TractFormer: 3D Direct Volumetric White Matter Tract Segmentation with Hybrid Channel-Wise Transformer.Sensors (Basel, Switzerland) · 2026Article
- Effect of a consistent reconstruction algorithm on inter-scanner reproducibility in diffusion MRI.Medical physics · 2025Article
- A software ecosystem for brain tractometry processing, analysis, and insight.PLoS computational biology · 2025Article
- Tractography-Based Automated Identification of Retinogeniculate Visual Pathway With Novel Microstructure-Informed Supervised Contrastive Learning.Human brain mapping · 2024Article
- Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI.Human brain mapping · 2024Article
- A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations.Scientific data · 2024Article
- DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.IEEE transactions on medical imaging · 2024Article
- Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study.Scientific data · 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
- Phybers: a package for brain tractography analysis.Frontiers in neuroscience · 2024Article
- Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.Human brain mapping · 2023Article
- FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.Computational diffusion MRI. CDMRI (Workshop) · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
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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.