Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study
Jeremy F. Strain,
Maryam Rahmani,
Donna Dierker,
Christopher Owen,
Hussain Jafri,
Andrei G. Vlassenko,
Kyle Womack,
Jurgen Fripp,
Duygu Tosun,
Tammie L.S. Benzinger,
Michael Weiner,
Colin Masters,
Jin-Moo Lee,
John C. Morris,
Manu S. Goyal
Affiliations
Jeremy F. Strain
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA; Corresponding author.
Maryam Rahmani
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
Donna Dierker
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
Christopher Owen
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Hussain Jafri
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Andrei G. Vlassenko
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
Kyle Womack
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
Jurgen Fripp
The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia
Duygu Tosun
Division of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, USA
Tammie L.S. Benzinger
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
Michael Weiner
Division of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, USA
Colin Masters
The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
Jin-Moo Lee
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
John C. Morris
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
Manu S. Goyal
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
White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.
