Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …

Skeletal muscle size is an important factor in assessing adaptation to exercise training and
detraining, athletic performance, age-associated atrophy and mobility decline, clinical …

By leveraging the recent development of artificial intelligence algorithms, several medical
sectors have benefited from using automatic segmentation tools from bioimaging to segment …

Due to the influence of kidney morphology, heterogeneous structure and image quality,
segmenting kidney in ultrasound images is challenging. To alleviate this challenge, we …

Automatically measuring a muscle's cross-sectional area is an important application in
clinical practice that has been studied extensively in recent years for its ability to assess …

Semantic medical image segmentation is a crucial part of both scientific research and
clinical care. With enough labelled data, deep learning models can be trained to accurately …

The study describes a protocol for methylation analysis integrated with Machine Learning
(ML) algorithms developed to classify Facio-Scapulo-Humeral Dystrophy (FSHD) subjects …

Ultrasonography (US) has become a valuable imaging tool for the examination of the
musculoskeletal system. It provides important diagnostic information and it can also be very …

Ultrasound (US) examination of the fetal central nervous system is one of the significant
tasks in the mid-term pregnancy inspection, in which the fetal cerebellum, as the key …

Musculoskeletal ultrasound imaging is an important basis for the early screening and
accurate treatment of muscle disorders. It allows the observation of muscle status to screen …