Muscle Cross-Sectional Area Segmentation in Transverse Ultrasound Images Using Vision Transformers.

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 muscle architecture. Additionally, an adequately segmented cross-sectional area can be used to estimate the echogenicity of the muscle, another valuable parameter correlated with muscle quality. This study assesses state-of-the-art convolutional neural networks and vision transformers for automating this task in a new, large, and diverse database.

Radiotherapy and Renal Cell Carcinoma: A Continuing Saga.

Renal cell carcinoma (RCC) is one of the most aggressive malignancies of the genito-urinary tract, having a poor prognosis especially in patients with metastasis. Surgical resection remains the gold standard for localized renal cancer disease, with radiotherapy (RT) receiving much skepticism during the last decades. However, many studies have evaluated the role of RT, and although renal cancer is traditionally considered radio-resistant, technological advances in the RT field with regards to modern linear accelerators, as well as advanced RT techniques have resulted in breakthrough therapeutic outcomes.

Reliability of muscle thickness measurements in ultrasonography.

This study aims to clarify some of the issues associated with the reliable measurement of muscle thickness on ultrasonographic images of the musculoskeletal system, namely the repeatability of measurements in different time frames, the effect of body side selection, and the effect of scan orientation. Ultrasound scans were performed on muscles associated with essential daily activities: geniohyoid, masseter, anterior arm muscles, rectus femoris, vastus intermedius, tibialis anterior, and gastrocnemius. Measurements of the muscle thickness were performed and repeated after 1, 6, and 24 h, on both dominant and nondominant side, using both transverse and longitudinal scans.

Muscle Type and Gender Recognition Utilising High-Level Textural Representation in Musculoskeletal Ultrasonography.

Human assistive technology and computer-aided diagnosis is an emerging field in the area of medical imaging. Following the recent advances in this domain, a study for integrating machine learning techniques in musculoskeletal ultrasonography images was conducted. The goal of this attempt was to investigate how feature extraction techniques, that capture higher-level information, perform in identifying human characteristics.