Article ID Journal Published Year Pages File Type
8686942 NeuroImage 2018 57 Pages PDF
Abstract
We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI) = 0.848) and volumetric (intraclass correlation coefficient (ICC) = 0.985) agreement between the UBO Detector-derived and manually traced WMH; b) highly correlated (r2 > 0.9) and a steady increase of WMH volumes over time; and c) significant associations of periventricular (t = 22.591, p < 0.001) and deep (t = 14.523, p < 0.001) WMH volumes generated by UBO Detector with Fazekas rating scores. With parallel computing enabled in UBO Detector, the processing can take advantage of multi-core CPU's that are commonly available on workstations. In conclusion, UBO Detector is a reliable, efficient and fully automated WMH segmentation pipeline.
Related Topics
Life Sciences Neuroscience Cognitive Neuroscience
Authors
, , , , , , , ,