ArticleHuman brain mapping2019
Automated brain extraction of multisequence MRI using artificial neural networks.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 364 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
364 citing papers in PubMed.
- Exercise-induced brain changes in cannabis use disorder: a longitudinal MRI study of a 12-week supervised HIIT program.Molecular psychiatry · 2026Trial
- A Randomized Trial of Shunting for Idiopathic Normal-Pressure Hydrocephalus.The New England journal of medicine · 2025Trial
- Treatment effects of cladribine tablets on data-driven patterns of regional grey matter atrophy in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Trial
- Amide proton transfer-weighted CEST MRI for radiotherapy target delineation of glioblastoma: a prospective pilot study.European radiology experimental · 2024Trial
- Recurrence distance analysis for clinical target volume optimization in glioblastoma.Clinical and translational radiation oncology · 2026Article
- Improved diffusion tensor parametric maps in the brain from reduced-direction acquisitions using a deep learning approach.Magma (New York, N.Y.) · 2026Article
- Article
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- PREDICT-GBM: A multicenter platform advancing personalized glioblastoma radiotherapy planning.NPJ digital medicine · 2026Article
- Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer's Disease: A Preprocessing-Focused Pipeline.Journal of imaging · 2026Article
- MARBLE: An in vivo MRI marker of limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Threshold Effects, Cognitive Decline, and Longitudinal Changes in White Matter Hyperintensity Volume.Stroke · 2026Article
- From Anatomy to Outcome: Linking Glioma Location Patterns to Survival Using Non-Negative Matrix Factorization.Clinical neuroradiology · 2026Article
- Detection of Lipid-Mediated Diffused Pathological Changes in Multiple Sclerosis.Research square · 2026Article
- Altered Cortical Gyrification Is Associated with Cortical and White Matter Microstructure in Individuals with Early Psychosis: A Multimodal Neuroimaging Study.Schizophrenia bulletin · 2026Article
- Whole-brain histogram analysis and top 20% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale.Annals of nuclear medicine · 2026Article
- Altered T1w/T2w-FLAIR Ratio in White Matter Hyperintensities as an Indicator of Structural Integrity Loss: Association with Alzheimer's Disease and Vascular Dementia.medRxiv : the preprint server for health sciences · 2026Article
- Article
- A comparative evaluation of brain MRI modalities, modality-specific measures, brain regions, and cognitive tests for brain age prediction.GeroScience · 2026Article
- Mediation Analysis Between Brain Age, Disease-Modifying Factors, and Disability and Cognitive Performance in Multiple Sclerosis.Neurology · 2026Article
304 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm, which shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD-BET prediction algorithm is made freely available (www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.
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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 OpenQuestion graph.