NeuroImage. Clinical · 2026
Effect of vascular lesion preprocessing on Brain Intensity AbNormality Classification Algorithm (BIANCA) white matter hyperintensity segmentation.
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Temuulen Uchralt, Mekle Ralf, Galinovic Ivana, Weber Joachim E, Landmesser Ulf, Kelle Sebastian, Endres Matthias, Stehning Christian, Villringer Kersten, BeLOVE Study Group
Аннотация
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.