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

Introduction: Over the past few years, the deep learning community has developed and
validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis …

We present a study of multiple sclerosis segmentation algorithms conducted at the
international MICCAI 2016 challenge. This challenge was operated using a new open …

The performance of current machine learning methods to detect heterogeneous pathology is
limited by the quantity and quality of pathology in medical images. A possible solution is …

Introduction Lesion segmentation plays an important role in the diagnosis and follow-up of
multiple sclerosis (MS). This task is very time-consuming and subject to intra-and inter-rater …

Purpose To develop a method to automatically detect multiple sclerosis (MS) lesions,
located both in white matter (WM) and in the cortex, in patients with low disability and early …

The quantification of new or enlarged lesions from follow-up MRI scans is an important
surrogate of clinical disease activity in patients with multiple sclerosis (MS). Not only is …

Magnetic resonance imaging (MRI) synthesis has attracted attention due to its various
applications in the medical imaging domain. In this paper, we propose generating synthetic …

Introduction Longitudinal magnetic resonance imaging (MRI) analysis has an important role
in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain …

Multiple sclerosis (MS) lesion segmentation is crucial for evaluating disease burden,
determining disease progression and measuring the impact of new clinical treatments. MS …