Magnetic resonance (MR) imaging is often used to characterize and quantify multiple
sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have …

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely
investigated in recent years with the goal of helping MS diagnosis and patient follow-up …

Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …

We propose a novel segmentation approach based on deep 3D convolutional encoder
networks with shortcut connections and apply it to the segmentation of multiple sclerosis …

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple
sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its …

Manual segmentation of multiple sclerosis (MS) in brain imaging is a challenging task due to
intra and inter-observer variability resulting in poor reproducibility. To overcome the …

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 …

In the analysis of brain Magnetic Resonance Images (MRI), classification of normality and
abnormality is an important issue. Many works have been done to classify the brain MR …

Machine learning is a popular method for mining and analyzing large collections of medical
data. We focus on a particular problem from medical research, supervised multiple sclerosis …

Goal: In this paper, a fully automatic probabilistic method for multiple sclerosis (MS) lesion
classification is presented, whereby the posterior probability density function over healthy …