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

Background: Computer-aided diagnosis can facilitate the early detection and diagnosis of
multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS …

In this study, a survey of multiple sclerosis (MS) classification and segmentation process is
presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is …

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 …

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 …

Multiple Sclerosis (MS) could be considered as one of the most serious neurological
diseases that can cause damage to the central nervous system. Such pathology has …

A crucial step in the clinical adaptation of an AI-based tool is an external, independent
validation. The aim of this study was to investigate brain atrophy in patients with confirmed …

Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis.
Experts usually perform this detection process manually, but there is interest in automating it …

The exploration and diagnosis of severe neuropathologies is one of the essential tasks that
could be reinforced and aided by advanced techniques in medical imagery. Cortex lesions …

Precise segmentation of magnetic resonance image (MRI) seems challenging because of
the complex structure of the brain, non-uniform field in images, and noise. As a result …