Data Sheet 1_Comparison of automated white matter lesion segmentation approaches for use in large, multi-site data analyses in Parkinson’s disease.docx

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.</p>Results<p>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.</p>Conclusion<p>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.</p>