Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study.

ABSTRACT: 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

SUBMITTER: Strain JF 

PROVIDER: S-EPMC11534282 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature