Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8686942 | NeuroImage | 2018 | 57 Pages |
Abstract
We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI)â¯=â¯0.848) and volumetric (intraclass correlation coefficient (ICC)â¯=â¯0.985) agreement between the UBO Detector-derived and manually traced WMH; b) highly correlated (r2â¯>â¯0.9) and a steady increase of WMH volumes over time; and c) significant associations of periventricular (tâ¯=â¯22.591, pâ¯<â¯0.001) and deep (tâ¯=â¯14.523, pâ¯<â¯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.
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Authors
Jiyang Jiang, Tao Liu, Wanlin Zhu, Rebecca Koncz, Hao Liu, Teresa Lee, Perminder S. Sachdev, Wei Wen,