White matter hyperintensities volume and cognition: Assessment of a deep learning based lesion detection and quantification algorithm on the Alzheimers Disease Neuroimaging Initiative

TL;DR

This study evaluates a deep learning algorithm, StackGen-Net, for detecting and quantifying white matter hyperintensities in brain MRI scans, demonstrating its accuracy and clinical relevance in Alzheimer's disease research.

Contribution

The paper introduces a novel deep learning-based WMH segmentation algorithm and validates its accuracy against expert manual segmentations on ADNI data.

Findings

WMH volume correlates with cognitive decline in ADNI subjects.

StackGen-Net achieves high accuracy in WMH detection.

Larger WMH volumes are associated with worse cognitive performance.

Abstract

The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used. As such, accurate detection and quantification of WMH is of great interest. Here, we use a deep learning-based WMH segmentation algorithm, StackGen-Net, to detect and quantify WMH on 3D FLAIR volumes from ADNI. We used a subset of subjects (n=20) and obtained manual WMH segmentations by an experienced neuro-radiologist to demonstrate the accuracy of our algorithm. On a larger cohort of subjects (n=290), we observed that larger WMH volumes correlated with worse performance on executive function (P=.004), memory (P=.01), and language (P=.005).