Optimizing automated white matter hyperintensity segmentation in individuals with stroke

Jennifer K Ferris; Bethany P Lo; Mohamed Salah Khlif; Amy Brodtmann; Lara A Boyd; Sook-Lei Liew

doi:10.3389/fnimg.2023.1099301

Optimizing automated white matter hyperintensity segmentation in individuals with stroke

Front Neuroimaging. 2023 Mar 9:2:1099301. doi: 10.3389/fnimg.2023.1099301. eCollection 2023.

Authors

Jennifer K Ferris^{1

2}, Bethany P Lo³, Mohamed Salah Khlif⁴, Amy Brodtmann^{4

5}, Lara A Boyd^{1

6}, Sook-Lei Liew^{3

7}

Affiliations

¹ Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada.
² Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada.
³ Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
⁴ Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia.
⁵ Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia.
⁶ Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
⁷ Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.

Abstract

White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.

Keywords: BIANCA; FSL; SAMSEG; lesion segmentation; stroke; white matter hyperintensity (WMH).

Abstract

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