A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
We propose a novel unsupervised approach of detecting and segmenting white matter abnormalities, using limited one-time sampling irregularity age map (LOTS-IAM). LOTS-IAM is a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI) (e.g. T2-FLAIR white matter hyperintensities (WMH)). In this study, the limited one-time sampling scheme is proposed and implemented on GPU. We compared the performance of LOTS-IAM in detecting and segmentingdoi:10.1101/334292 fatcat:ouw5mtiobjeghi6zqs56v7payq