ArticleNeuroImage. Clinical2026
Effect of vascular lesion preprocessing on Brain Intensity AbNormality Classification Algorithm (BIANCA) white matter hyperintensity segmentation.
Article in NeuroImage. Clinical, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundWhite 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.
methodsWe 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).
resultsBIANCA 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.
conclusionsBIANCA 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.
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