Journal Article

DZNE-2026-00540

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Effect of vascular lesion preprocessing on Brain Intensity AbNormality Classification Algorithm (BIANCA) white matter hyperintensity segmentation

Temuulen, U. ; Mekle, R. ; Galinovic, I. ; Weber, J. E. ; Landmesser, U. ; Kelle, S. ; Endres, M.DZNE* ; Stehning, C. ; Villringer, K.

2026
Elsevier [Amsterdam u.a.]

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Please use a persistent id in citations: doi:10.1016/j.nicl.2026.104001

Abstract: White matter hyperintensity (WMH) segmentation using BIANCA (Brain Intensity AbNormality Classification Algorithm) in stroke populations is complicated by vascular lesions that share T2-hyperintense signal characteristics with WMH. Whether preprocessing decisions in the treatment of lesions affect segmentation accuracy has not yet been systematically evaluated in cerebrovascular cohorts involving multiple scanners.We compared fixed probability thresholds and locally adaptive thresholding with LOCATE (LOCally Adaptive Threshold Estimation), and three lesion-handling approaches: lesions present (non removed), replaced with zero intensities (removed), and replaced with normal-appearing white matter intensities (NAWM; inpainted), using the BeLOVE cohort (Berlin Longterm Observation of Vascular Events) and the WMH Segmentation Challenge dataset. Phase I (n = 89) optimized thresholding via stratified 5-fold cross-validation. Phase II assessed preprocessing effects on segmentation accuracy (Phase II-A, n = 89) and volume agreement (Phase II-B, n = 211).BIANCA with LOCATE adaptive thresholding achieved moderate segmentation overlap (mean Dice 0.567), with lesion-level detection exceeding an F1 of 0.85. Preprocessing effects were statistically detectable but negligible in magnitude, with near-perfect agreement between all conditions. Stroke lesion volume was the highest-ranked predictor of volume differences between conditions; these scaled with lesion size but remained negligible in magnitude across all subgroups.BIANCA with LOCATE achieved moderate WMH segmentation performance with the best sensitivity-precision trade-off in this multi-scanner cerebrovascular cohort. Preprocessing effects were negligible at the group level. However, large lesions distort the FLAIR intensity distribution on which BIANCA relies for classification, which justifies lesion removal as a recommended preprocessing step.

Keyword(s): Automated segmentation ; BIANCA ; Cerebral small vessel disease ; FLAIR ; LOCATE ; Stroke ; White matterhyperintensities

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Contributing Institute(s):

1. Interdisciplinary Dementia Research (AG Endres)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

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