Gaubert, M., Dell’Orco, A., Lange, C. et al. (33 more authors) (2023) Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia. Frontiers in Psychiatry, 13. 1010273. ISSN 1664-0640
Abstract
Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial 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. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.
Methods: We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).
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 the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number 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, the 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Gaubert, Dell’Orco, Lange, Garnier-Crussard, Zimmermann, Dyrba, Duering, Ziegler, Peters, Preis, Priller, Spruth, Schneider, Fliessbach, Wiltfang, Schott, Maier, Glanz, Buerger, Janowitz, Perneczky, Rauchmann, Teipel, Kilimann, Laske, Munk, Spottke, Roy, Dobisch, Ewers, Dechent, Haynes, Scheffler, Düzel, Jessen and Wirth. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | Acquired Cognitive Impairment; Brain Disorders; Neurosciences; Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD); Dementia; Alzheimer's Disease; Clinical Research; Aging; Neurodegenerative; Neurological |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Neuroscience (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Jan 2023 16:14 |
Last Modified: | 30 Jan 2023 16:14 |
Published Version: | http://dx.doi.org/10.3389/fpsyt.2022.1010273 |
Status: | Published |
Publisher: | Frontiers Media SA |
Refereed: | Yes |
Identification Number: | 10.3389/fpsyt.2022.1010273 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195793 |