Regional WMH Quantification

• Regional WMH Quantification is the systematic segmentation and mapping of white matter hyperintensities into discrete regions to improve clinical assessments.
• It employs advanced segmentation methods like 3D U-Net along with robust preprocessing and atlas-based techniques to ensure reproducibility and accuracy.
• This approach enhances disease classification by distinguishing lesion populations in periventricular and deep compartments, aiding in Alzheimer’s and vascular risk analyses.

White matter hyperintensities (WMH), highly conspicuous on T2-weighted and FLAIR MRI, are established imaging markers of small vessel disease, neurodegeneration, and cognitive decline. Regional WMH quantification refers to the systematic segmentation, anatomical mapping, and volumetric measurement of WMH within discrete white matter compartments, as opposed to global burden assessments that sum lesion load over the entire brain. This approach produces region-wise WMH volume or burden metrics with the intent to improve disease classification, model topographic vulnerability, and provide finely resolved imaging biomarkers for neurodegenerative and cerebrovascular disorders.

1. Anatomical Region Definitions and Parcellation Approaches

Contemporary regional WMH quantification relies on subdivision of white matter into anatomical or clinically relevant subregions. The most widely adopted atlases include the refined JHU MNI White Matter Atlas Type II, which defines 34 major tracts and subcompartments spanning association, projection, and commissural fibers, and the FreeSurfer segmentation (aseg) atlas, which supplies lobar and subcortical regions (Machnio et al., 18 Nov 2025, Machnio et al., 27 Jun 2025, Laso et al., 2023, Phitidis et al., 22 Oct 2024).

For periventricular versus deep WMH (PVWMH, DWMH), ROIs are assigned by Euclidean distance from the lateral ventricles, with thresholds commonly set at <12 mm (PVWMH) or ≥12 mm (DWMH), or, more granularly, as concentric rings (e.g., 0–5 mm, 5–10 mm, 10–15 mm, ≥15 mm) (Alqarni et al., 2022, Philps et al., 26 Nov 2024). Such spatial compartmentalization serves both to distinguish morphologically and etiologically distinct lesion populations and to reduce inter-individual anatomic variability via atlas-based normalization.

2. Segmentation Algorithms and Preprocessing Pipelines

Automated WMH segmentation is the foundational step, and current state-of-the-art methods employ deep convolutional neural networks, primarily 3D U-Net architectures, often in dual or multi-task configurations (Machnio et al., 18 Nov 2025, Machnio et al., 27 Jun 2025, Laso et al., 2023). Preprocessing protocols consistently include skull-stripping, N4 bias field correction, and intensity normalization, followed by affine or rigid co-registration of FLAIR/T1 sequences and atlas templates to subject native space.

Training leverages composite losses combining voxel-wise cross-entropy with Dice–Sørensen overlap, with typical batch sizes of 2–4 volumes and strong MRI-specific augmentations (additive noise, synthetic bias, elastic deformations, simulated motion), to enforce robustness across acquisition protocols and lesion burden variability (Machnio et al., 18 Nov 2025, Machnio et al., 27 Jun 2025). Various methods address multi-modality input (e.g., concatenated FLAIR+T1, modality-interchangeable pipelines), as well as domain randomization for MRI types and field strengths (WMH-SynthSeg) (Laso et al., 2023). Rule-based and classical ML pipelines, using histogram thresholds and tissue masks (e.g., UBO Detector, FAST, FreeSurfer) remain common in large-scale, resource-constrained biobank analyses (Alqarni et al., 2022, Phitidis et al., 22 Oct 2024).

3. Region-Wise WMH Volume Computation

The quantification of regional lesion burden universally follows the summation of binarized lesion voxels restricted to each anatomical compartment, scaled by voxel volume. For region $r$, this is formalized as:

$$V_{\mathrm{WMH}, r} = \sum_{v \in V_r} I_{\mathrm{WMH}}(v) \cdot V_{\text{voxel}}$$

where $V_r$ denotes all voxels in region $r$, $I_{\mathrm{WMH}}(v)$ is the binary WMH indicator, and $V_{\text{voxel}}$ is the physical volume per voxel (Machnio et al., 18 Nov 2025, Laso et al., 2023, Machnio et al., 27 Jun 2025, Phitidis et al., 22 Oct 2024).

For periventricular and deep compartments:

$$V_{\mathrm{WMH}}^{\mathrm{PV}} = \sum_{i} \mathrm{WMH}_i \cdot \mathrm{PV}_i \cdot V_{\text{voxel}};\quad V_{\mathrm{WMH}}^{\mathrm{D}} = \sum_{i} \mathrm{WMH}_i \cdot D_i \cdot V_{\text{voxel}}$$

with $\mathrm{PV}_i, D_i$ encoding binary membership to PV or deep regions (Alqarni et al., 2022, Philps et al., 26 Nov 2024).

Relative regional burden is either calculated as a proportion of the regional white matter, $B_r = V_{\mathrm{WMH}, r}/V_{\text{region}, r}$, or normalized by intracranial volume (ICV) for cross-subject comparison:

$$V_{\mathrm{WMH}, r}^{\mathrm{norm}} = V_{\mathrm{WMH}, r} / \mathrm{ICV}$$

(Machnio et al., 18 Nov 2025, Machnio et al., 27 Jun 2025, Laso et al., 2023, Phitidis et al., 22 Oct 2024).

Commercial systems and research tools converge on these formulas, sometimes additionally reporting region-wise annualized growth ($G_i = \Delta V_i / \Delta t$), and test–retest ICC ($>0.9$) for both cross-sectional and longitudinal contexts (Phitidis et al., 22 Oct 2024).

4. Validation, Accuracy Metrics, and Quality Control

Validation is routinely performed using expert-annotated public datasets and cohort-specific manual segmentations (e.g., MICCAI 2017, ADNI, UK Biobank), reporting global and region-specific Dice coefficients (typically 0.72–0.85 for whole-brain, up to 0.87–0.90 for lobar or subregional WMH), absolute volume differences (AVD), and intraclass correlation coefficients (ICC) for volumetric reproducibility (Machnio et al., 18 Nov 2025, Machnio et al., 27 Jun 2025, Laso et al., 2023, Phitidis et al., 22 Oct 2024).

Uncertainty quantification (UQ) has been introduced through stochastic segmentation networks and deep ensembles (e.g., SSN-Ens), yielding not only higher Dice and lower AVD on both in-domain and out-of-distribution data, but enabling automated detection of low-quality segmentations and improved downstream clinical classification (Fazekas scoring) (Philps et al., 26 Nov 2024). False positive WMH burden in controls is generally below 1 ml for best-in-class models (Laso et al., 2023).

5. Clinical and Research Applications

Regional WMH quantification outperforms global metrics for disease classification, specifically in Alzheimer's disease (AD) versus cognitively normal (CN) or mild cognitive impairment (MCI). In cross-validated analyses, regional WMH feature sets achieve higher AUC for distinguishing AD from CN (0.87 regional vs. 0.72 global WMH), and the combination with brain atrophy measures yields AUC up to 0.97 (Machnio et al., 18 Nov 2025). Certain white matter subregions, such as the superior parietal lobule/angular/supramarginal gyrus, fronto-orbital cortex, inferior frontal gyrus, superior longitudinal fasciculus, and posterior limb of the internal capsule/cerebral peduncle, are reproducibly associated with AD status, with Bonferroni-corrected $p<0.05$ in regression models.

In vascular risk modeling, regional WMH analyses reveal dissociable patterns: PVWMH is more strongly linked to BMI and hypertension, DWMH to pulse wave velocity and, critically, interacts with hormonal risk factors such as testosterone in men and HRT duration/menopause timing in women (Alqarni et al., 2022). This convergence underscores the clinical utility of topographically resolved lesion metrics in both neurodegeneration and small vessel disease paradigms.

Regional WMH burden and topography also inform prognostication of cognitive decline and stroke risk, with high regional ICCs ($>$0.9) and Dices ($>$0.8) supporting their use as reproducible imaging biomarkers in both clinical trials and observational studies (Phitidis et al., 22 Oct 2024).

6. Methodological Challenges and Solutions

Key methodological obstacles include registration-induced misassignment of lesion voxels across atlas boundaries, inter-regional variability in lesion conspicuity (with deep lesions often less conspicuous), intensity inhomogeneity, atlas parcellation accuracy, and deep learning insensitivity to small, punctate clusters. Suggested solutions comprise:

• Rigid/affine/nonlinear registration with mutual information cost, or within-subject template generation for longitudinal data.
• Region-adaptive thresholding or multi-stage CNNs encoding spatial location.
• N4-ITK bias correction, WM peak intensity alignment, and synthetic data augmentation.
• Multi-atlas or dual-compartment approaches (PVWMH/DWMH lobar).
• Multi-scale CNNs with dilated convolutions, attention, or residual blocks targeting detection and refinement of small lesions (Phitidis et al., 22 Oct 2024, Machnio et al., 27 Jun 2025).

Longitudinal tracking protocols employ per-visit segmentation and volume differencing or fit linear mixed-effects models for region-wise slope estimation, yielding relative error $<$5% in annualized burden rates (Phitidis et al., 22 Oct 2024).

7. Commercial, Research, and Technical Systems

The regional WMH quantification workflow has been widely implemented in commercial neuroimaging software, with cMRI, NeuroQuant, icobrain-dm, AQUA, mdbrain, Pixyl .Neuro, Quantib ND, QUBIOtech, QP-Brain, QyScore, and VUNO DeepBrain among notable systems (Phitidis et al., 22 Oct 2024). These tools combine FLAIR-based segmentation (CNN or rule-based) with atlas parcellation and standardized volume formulas, reporting reproducibility metrics of DSC $>$0.75 and ICC $>$0.90 for lobar and PV/D compartments, with rigorous preprocessing and template harmonization enforced as preconditions for robust output.

Research platforms such as WMH-SynthSeg enable multi-contrast, multi-resolution regional metrics without retraining on new scanners or protocols, supporting large-scale and portable MRI studies via domain-randomized synthetic training (Laso et al., 2023). Region segmentation granularity, from lobar to tract-level, is attainable with refined atlases, but carries potential for partial-volume mislabeling and boundary effects, particularly in highly atrophic or distorted brains.

The field continues to evolve toward unified multi-marker analysis encompassing WMH, infarcts, atrophy, and microbleeds; however, comprehensive, openly validated algorithms for joint CVD marker quantification remain unavailable (Phitidis et al., 22 Oct 2024). Advances in uncertainty modeling, multi-task architectures, and harmonized data augmentation are current areas of methodological development.