ArticleNeuroImage2021
Deep learning based segmentation of brain tissue from diffusion MRI.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The diagnostic and prediction performance of MR diffusion kurtosis imaging in the glioma molecular classification: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Deep learning-based Desikan-Killiany parcellation of the brain using diffusion MRI.Scientific reports · 2026Article
- Anatomically constrained and curated cerebellar tractography (ACCURAT): an open framework and a pathway-specific neuroanatomical reference.bioRxiv : the preprint server for biology · 2026Article
- Detailed Delineation of the Fetal Brain in Diffusion MRI via Multi-Task Learning.IEEE transactions on medical imaging · 2026Article
- A review on learning-based algorithms for tractography and human brain white matter tracts recognition.Neuroradiology · 2025Review
- Towards an Informed Choice of Diffusion MRI Image Contrasts for Cerebellar Segmentation.Human brain mapping · 2025Article
- Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.Chinese medical journal · 2025Review
- DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Excessive interstitial free-water in cortical gray matter preceding accelerated volume changes in individuals at clinical high risk for psychosis.Molecular psychiatry · 2024Article
- Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI.Human brain mapping · 2024Article
- Article
- Anatomically constrained tractography of the fetal brain.NeuroImage · 2024Article
- Impact of Systolic Blood Viscosity on Deep White Matter Hyperintensities in Patients With Acute Ischemic Stroke.Journal of the American Heart Association · 2024Article
- The Mexican dataset of a repetitive transcranial magnetic stimulation clinical trial on cocaine use disorder patients: SUDMEX TMS.Scientific data · 2024Article
- DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.IEEE transactions on medical imaging · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- DiMANI: diffusion MRI for anatomical nuclei imaging-Application for the direct visualization of thalamic subnuclei.Frontiers in human neuroscience · 2024Article
- Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.bioRxiv : the preprint server for biology · 2023Article
- Microstructural Cortical Gray Matter Changes Preceding Accelerated Volume Changes in Individuals at Clinical High Risk for Psychosis.Research square · 2023Article
- Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learning.Eye (London, England) · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.
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Registered trials
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 Socratic graph.