Background White matter hyperintensities (WMH), of presumed vascular origin, are visible
and quantifiable neuroradiological markers of brain parenchymal change. These changes …

Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for
segmenting brain lesions with considerable mass-effect, to segment white matter …

Medical imaging is the most established technique of visualizing the interior of the human
body without the risk of the non-invasive effect. This technology is designed to produce …

Background and objectives Multiple sclerosis is one of the major diseases and the
progressive MS lesion formation often leads to cognitive decline and physical disability. A …

Recent emerging hybrid technology of positron emission tomography/magnetic resonance
(PET/MR) imaging has generated a great need for an accurate MR image-based PET …

Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion,
which describes the blood's passage through the brain's vascular network. Therefore, it is …

White matter lesions (WML) underlie multiple brain disorders, and automatic WML
segmentation is crucial to evaluate the natural disease course and effectiveness of clinical …

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease
(SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme …

In this paper, we present a level set method for multiple sclerosis (MS) lesion segmentation
from FLAIR images in the presence of intensity inhomogeneities. We use a three-phase …