We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully
automatic unsupervised approach to extract brain tissue irregularities in magnetic …

Many algorithms have been proposed for automated segmentation of white matter
hyperintensities (WMH) in brain MRI. Yet, broad uptake of any particular algorithm has not …

In the wake of the use of deep learning algorithms in medical image analysis, we compared
performance of deep learning algorithms, namely the deep Boltzmann machine (DBM) …

Precise detection and quantification of white matter hyperintensities (WMH) is of great
interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel …

Brain vascular damage accumulate in aging and often manifest as white matter
hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to …

White matter hyperintensities (WMH) are imaging manifestations frequently observed in
various neurological disorders, yet the clinical application of WMH quantification is limited. In …

Accurate quantification of white matter hyperintensities (WMH) from Magnetic Resonance
Imaging (MRI) is a valuable tool for the analysis of normal brain ageing or …

Brain white matter hyperintensities (WMHs) are linked to increased risk of cerebrovascular
and neurodegenerative diseases among the elderly. Consequently, detection and …

This study proposed a semisupervised loss function named level‐set loss (LSLoss) for
cerebral white matter hyperintensities (WMHs) segmentation on fluid‐attenuated inversion …

Different types of white matter hyperintensities (WMH) can be observed through MRI in the
brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from …