Background Magnetic resonance imaging (MRI) is the dominant 3D imaging modality to
quantify muscle properties in skeletal muscle disorders, in inherited and acquired muscle …

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

Neuromuscular disorders are rare diseases for which few therapeutic strategies currently
exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers …

Background Deep learning methods have been shown to be useful for segmentation of
lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on …

Background The rotator cuff (RC) is a crucial anatomical element within the shoulder joint,
facilitating an extensive array of motions while maintaining joint stability. Comprised of the …

Muscular dystrophies (MD) are a class of rare genetic diseases resulting in progressive
muscle weakness affecting specific muscle groups, depending on the type of disease …

Individual muscle segmentation is the process of partitioning medical images into regions
representing each muscle. It can be used to isolate spatially structured quantitative muscle …

Objective In this study we address the automatic segmentation of selected muscles of the
thigh and leg through a supervised deep learning approach. Material and methods The …

Diagnosis of peripheral neuropathies relies on neurological examinations, electrodiagnostic
studies, and since recently magnetic resonance neurography (MRN). The aim of this study …

Muscle segmentation is a process relied upon to gather medical image-based muscle
characterisation, useful in directly assessing muscle volume and geometry, that can be used …