76 Benchmarking automated segmentation tools for white matter hyperintensities quantification to inform stroke and dementia risk stratification

Objectives/Goals: To benchmark the extent to which variability in automated WMH quantification influences stroke and dementia risk stratification by characterizing differences in volume estimates and spatial concordance across three segmentation tools (BIANCA, SAMSEG, and nnU-Net). Methods/Study Population: This retrospective, cross-sectional study will analyze de-identified clinical brain MRI scans from patients aged ≥50 years, including those with and without prior stroke, obtained from institutional imaging repositories. Eligible scans include co-registered T1-weighted and FLAIR sequences. Scans with poor image quality and scans from patients with multiple sclerosis, other demyelinating disorders, or brain tumors will be excluded. WMHs will be segmented using three tools: BIANCA (supervised k-NN classifier), SAMSEG (unsupervised Bayesian model), and nnU-Net (self-configuring deep learning). Agreement in WMH burden will be assessed with intraclass correlation coefficients (ICC[3,1]) and spatial concordance with Dice similarity coefficients (DSC). Bland-Altman analyses will evaluate bias. Results/Anticipated Results: We will analyze 600 brain MRI scans. All three tools are expected to detect similar WMH spatial distributions, though total WMH volume estimates may differ. nnU-Net may yield higher WMH burden estimates than BIANCA or SAMSEG. WMH burden will be neuroradiologist-adjudicated to establish the gold standard, against which concordance for each tool and inter-tool agreement will be assessed using ICC[3,1] and DSC. We anticipate moderate-to-high agreement, but tool-specific variability could influence reproducibility and interpretation of WMH burden for future WMH-related research studies. This study will quantify the extent of variability among tools in clinical brain MRI scans and evaluate whether these differences could meaningfully alter future stroke risk classification thresholds. Discussion/Significance of Impact: This study will benchmark automated segmentation tools for WMH-related research by standardizing WMH quantification to improve reproducibility and reliability for stroke and dementia risk stratification. Establishing tool-specific benchmarks may also guide integration of automated WMH measures into clinical decision-support systems.