Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of
artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A …

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

Simple Summary Non-invasive imaging modalities are commonly used in clinical practice.
Recently, the application of machine learning (ML) techniques has provided a new scope for …

Fully automated approaches based on convolutional neural networks have shown
promising performances on muscle segmentation from magnetic resonance (MR) images …

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 …

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 …

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 Accurate segmentation of pelvic bones is an initial step to achieve accurate
detection and localisation of pelvic bone metastases. This study presents a deep learning …

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 …

Purpose Automatic muscle segmentation is critical for advancing our understanding of
human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely …