mlsanityData to 7 Oct 2026

Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI

arXiv 2312.05119 ↗First posted 8 Dec 2023eess.IV cs.CVcited by 15

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Abstract (arXiv)

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH (ρ=.85) and hippocampal volumes (r=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 7 of its 26 references that are in the map. First posted on arXiv 8 Dec 2023.

Builds on

7 of 26 references.

References · 7

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018Group Normalizationcited by 1,514
  3. 2018Deep Visual Domain Adaptation: A Surveycited by 634
  4. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 235
  5. 2019FastSurfer -- A fast and accurate deep learning based neuroimaging pipelinecited by 110
  6. 2018QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomycited by 37
  7. 2016Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensitiescited by 13

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Citing papers per year: 2024: 1; 2025: 7; 2026: 7.

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  1. 2024PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRIcited by 8
  2. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomizationcited by 6
  3. 2025Domain-randomized deep learning for neuroimage analysiscited by 6
  4. 2025A Modality-agnostic Multi-task Foundation Model for Human Brain Imagingcited by 5
  5. 2025Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomizationcited by 4
  6. 2025From Low Field to High Value: Robust Cortical Mapping from Low-Field MRIcited by 2
  7. 2025UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentationcited by 2
  8. 2025HULFSynth : An INR based Super-Resolution and Ultra Low-Field MRI Synthesis via Contrast factor estimationcited by 0
  9. 2026Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix Itcited by 0
  10. 2026TimeLesSeg: Unified Contrast-Agnostic Cross-Sectional and Longitudinal MS Lesion Segmentation via a Stochastic Generative Modelcited by 0
  11. 2026GLI-AL: A Multi-Modal Glioma MRI Label Resource with Unified Anatomy-Lesion Labelscited by 0
  12. 2026Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registrationcited by 0
3 more
  1. 2026Does FLAIR super-resolution erase or hallucinate small white-matter lesions?cited by 0
  2. 2026Uncertainty-Aware 3D Residual Wavelet Diffusion for Ultra Low-Field MRI Super-Resolutioncited by 0
  3. 2026Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settingscited by 0