Early evaluation of treatment response and prediction of disease evolution are key issues in
the management of people with multiple sclerosis (MS). In the past 20 years, MRI has …

Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …

The anatomical location of imaging features is of crucial importance for accurate diagnosis
in many medical tasks. Convolutional neural networks (CNN) have had huge successes in …

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion
segmentation challenge providing training and test data to registered participants. The …

The identification of intestinal dysbiosis in patients with neurological and psychiatric
disorders has highlighted the importance of gut–brain communication, and yet the question …

In this paper, we present an automated approach for segmenting multiple sclerosis (MS)
lesions from multi-modal brain magnetic resonance images. Our method is based on a deep …

In recent years, several convolutional neural network (CNN) methods have been proposed
for the automated white matter lesion segmentation of multiple sclerosis (MS) patient …

Purpose MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for
clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the …

Introduction: Brain atrophy measurement in multiple sclerosis (MS) has become an
important outcome for determining patients at risk for develo** physical and cognitive …