Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease

TL;DR

This paper introduces a deep learning framework for precise regional white matter hyperintensity mapping, which improves Alzheimer's disease diagnosis by leveraging spatial lesion distribution and combining it with brain atrophy measures.

Contribution

The study presents a novel deep learning method for regional WMH segmentation and demonstrates its superior diagnostic value over global measures in Alzheimer's disease.

Findings

Regional WMH volumes outperform global lesion load in disease classification

Integration with brain atrophy metrics enhances diagnostic accuracy (AUC up to 0.97)

Localized WMH regions are consistently associated with Alzheimer's diagnosis

Abstract

White matter hyperintensities (WMH) are key imaging markers in cognitive aging, Alzheimer's disease (AD), and related dementias. Although automated methods for WMH segmentation have advanced, most provide only global lesion load and overlook their spatial distribution across distinct white matter regions. We propose a deep learning framework for robust WMH segmentation and localization, evaluated across public datasets and an independent Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our results show that the predicted lesion loads are in line with the reference WMH estimates, confirming the robustness to variations in lesion load, acquisition, and demographics. Beyond accurate segmentation, we quantify WMH load within anatomically defined regions and combine these measures with brain structure volumes to assess diagnostic value. Regional WMH volumes consistently outperform global lesion burden for disease classification, and integration with brain atrophy metrics further improves performance, reaching area under the curve (AUC) values up to 0.97. Several spatially distinct regions, particularly within anterior white matter tracts, are reproducibly associated with diagnostic status, indicating localized vulnerability in AD. These results highlight the added value of regional WMH quantification. Incorporating localized lesion metrics alongside atrophy markers may enhance early diagnosis and stratification in neurodegenerative disorders.