ArticleFrontiers in neuroscience2026
Comparison of automated white matter lesion segmentation approaches for use in large, multi-site data analyses in Parkinson's disease.
Article in Frontiers in neuroscience, 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
Background: The vascular role in Parkinson's disease (PD) is emerging, yet the literature remains conflicted, motivating large-data analyses with greater statistical power. White matter lesions (WML) are an accepted imaging marker of small vessel disease. Accurate automated WML segmentation techniques are crucial for large-scale studies; however, evaluation of the optimum approach in PD is lacking. This study evaluated automated WML segmentation algorithms to determine the most accurate and reliable method, among those selected, for multi-site large data analysis in PD. Methods: We assessed whole-brain volumetric T1-weighted and FLAIR images from 201 PD patients (mean age, 66.6 ± 7.86 years) and 64 healthy controls (HC; mean age, 66.3 ± 8.67) across three datasets composed of different scanners, imaging parameters and lesion loads. WML were manually segmented to provide the gold standard, and four freely available automated algorithms were evaluated, FSL's BIANCA, FreeSurfer, SPM's LST-LPA and U-Net-pgs, using the performance metrics: Dice score, Hausdorff distance, recall, precision, F1 score, log absolute volume difference (LOGAVD) and intraclass correlation coefficient (ICC). Sub-analyses were performed based on lesion load, lobar regions and acquisition parameters. Results: U-Net-pgs produced the highest Dice score (PD: 0.46 ± 0.21; HC: 0.39 ± 0.21), recall (PD: 0.75 ± 0.24; HC: 0.58 ± 0.26), precision (PD: 0.48 ± 0.24; HC: 0.62 ± 0.25), F1 score (PD: 0.53 ± 0.21; HC: 0.54 ± 0.20) and ICC (PD: 0.87; HC: 0.89) and lowest Hausdorff distance (PD: 8.89 ± 3.96; HC: 6.33 ± 2.91) and LOGAVD (PD: 0.31 ± 0.31; HC 0.27 ± 0.30) across PD and HC. U-Net-pgs also showed overall superior performance in all lesion loads for PD and across brain regions in both PD and HC. Conclusion: Overall, of those we evaluated, U-Net-pgs emerged as the highest performing automated method across lesion loads and brain regions, for WML segmentation in PD and HC. The accuracy and reliability of U-Net-pgs, across various scanner and image acquisition parameters, make it a promising tool for large-scale analyses, facilitating future research investigating WML in PD.
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