Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia - Gaubert, Malo; Dell’Orco, Andrea; Lange, Catharina; Garnier-Crussard, Antoine; Zimmermann, Isabella; Dyrba, Martin; Duering, Marco; Ziegler, Gabriel; Peters, Oliver; Preis, Lukas; Priller, Josef; Spruth, Eike Jakob; et al. (Frontiers in Psychiatry, 2023)
Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’ disease (AD) and their advanced detection and quantification can be useful for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. The aim of this study was to compare the performance of segmentation tools and provide information on their application in multicenter research. Method: We used a pseudo-randomly selected dataset (n=50) from the DZNE multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D FLAIR images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation (BIANCA, LST LGA, LST LPA, pgs and sysu_media) were compared to manual reference segmentation. Results: Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed highest performances with an average Dice’s coefficient of 0.702 (±0.109) for volume and F1-score of 0.642 (±0.109 SD) for numbers of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. Conclusion: To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.