1. Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning.
- Author
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Rahmani M, Dierker D, Yaeger L, Saykin A, Luckett PH, Vlassenko AG, Owens C, Jafri H, Womack K, Fripp J, Xia Y, Tosun D, Benzinger TLS, Masters CL, Lee JM, Morris JC, Goyal MS, Strain JF, Kukull W, Weiner M, Burnham S, CoxDoecke TJ, Fedyashov V, Fripp J, Shishegar R, Xiong C, Marcus D, Raniga P, Li S, Aschenbrenner A, Hassenstab J, Lim YY, Maruff P, Sohrabi H, Robertson J, Markovic S, Bourgeat P, Doré V, Mayo CJ, Mussoumzadeh P, Rowe C, Villemagne V, Bateman R, Fowler C, Li QX, Martins R, Schindler S, Shaw L, Cruchaga C, Harari O, Laws S, Porter T, O'Brien E, Perrin R, Kukull W, Bateman R, McDade E, Jack C, Morris J, Yassi N, Bourgeat P, Perrin R, Roberts B, Villemagne V, Fedyashov V, and Goudey B
- Subjects
- Humans, Magnetic Resonance Imaging methods, Neuroimaging methods, Image Processing, Computer-Assisted methods, Brain diagnostic imaging, Brain pathology, Dementia diagnostic imaging, Aging physiology, Aging pathology, Deep Learning, White Matter diagnostic imaging, White Matter pathology
- Abstract
This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations., Competing Interests: Declarations. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Conflicts of interest: The authors declare that there are no conflicts of interest regarding the publication of this paper. This declaration is made to confirm that there has been no financial or personal relationship with other people or organizations that could inappropriately influence or bias the work presented in this manuscript. This includes, but is not limited to, employment, consultancies, stock ownership, honoraria, paid expert testimony, patent applications/registrations, and grants or other funding., (© 2024. The Author(s).)
- Published
- 2024
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