Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning.

Autor: Rahmani M; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO, USA., Dierker D; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO, USA., Yaeger L, Saykin A; Department School of Medicine, Indiana University, Bloomington, IN, USA., Luckett PH; Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA., Vlassenko AG; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.; Knight Alzheimer Disease Research Center, St. Louis, MO, USA.; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO, USA., Owens C; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA., Jafri H; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA., Womack K; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA., Fripp J; The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia., Xia Y; The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia., Tosun D; Division of Radiology and Biomedical Imaging, University of CA - San Francisco, San Francisco, CA, USA., Benzinger TLS; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.; Knight Alzheimer Disease Research Center, St. Louis, MO, USA., Masters CL; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia., Lee JM; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA., Morris JC; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.; Knight Alzheimer Disease Research Center, St. Louis, MO, USA., Goyal MS; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO, USA., Strain JF; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA. strainj@wustl.edu.; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO, USA. strainj@wustl.edu., 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, Goudey B
Jazyk: angličtina
Zdroj: Brain imaging and behavior [Brain Imaging Behav] 2024 Jul 31. Date of Electronic Publication: 2024 Jul 31.
DOI: 10.1007/s11682-024-00902-w
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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