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. This database consists of 2005 transverse ultrasound images from four informative muscles for neuromuscular disorders, recorded from 210 subjects of different ages, pathological conditions, and sexes. Regarding the reported results, all of the evaluated deep learning models have achieved near-to-human-level performance. In particular, the manual vs. the automatic measurements of the cross-sectional area exhibit an average discrepancy of less than 38.15 mm2, a significant result demonstrating the feasibility of automating this task. Moreover, the difference in muscle echogenicity estimated from these two readings is only 0.88, another indicator of the proposed method's success. Furthermore, Bland−Altman analysis of the measurements exhibits no systematic errors since most differences fall between the 95% limits of agreements and the two readings have a 0.97 Pearson's correlation coefficient (p < 0.001, validation set) with ICC (2, 1) surpassing 0.97, showing the reliability of this approach. Finally, as a supplementary analysis, the texture of the muscle's visible cross-sectional area was examined using deep learning to investigate whether a classification between healthy subjects and patients with pathological conditions solely from the muscle texture is possible. Our preliminary results indicate that such a task is feasible, but further and more extensive studies are required for more conclusive results.

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. Additionally, the combination of RT with immune checkpoint inhibitors and targeted agents may maximize the clinical benefit. This review article focuses on the role of RT in the therapeutic management of renal cell carcinoma.

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. Thirteen healthy volunteers (eight males and five females, mean age = 24 years, SD = 2.86, range = 19-29) were included. The intraclass correlation coefficient (ICC) was calculated between the baseline and the 1-, 6-, and 24-h interval, using a two-way mixed model of absolute agreement. The ICC ranged from 0.295 for the longitudinal scan of the left masseter muscle in the 6-h interval to 0.991 for the longitudinal scan of the nondominant anterior arm muscles in the 24-h interval. The results indicate that there is variable reliability of the measurements depending on the muscle, time frame, body side, and scan orientation. Consequently, the choice of these parameters can affect the validity of the measurements. Further investigation on a larger scale is required to establish the preferred parameters for each anatomical site.

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. The potential success of these techniques could lead to significant improvement of the current assessment methods-as the gray-scale image analysis-for distinguishing healthy and pathologic conditions, that are heavily dependent on the image-acquisition system. The contribution of this work is threefold. First, a new privately held data set of 74 healthy patients was presented. This data set included musculoskeletal ultrasound images from four muscles of the human body, namely the biceps brachii, tibialis anterior, gastrocnemius medialis and rectus femoris, recorded in the transverse and longitudinal plane. Second, two classification tasks were performed, namely, gender and muscle-type recognition, to assess the performance of the proposed method for successfully identifying differences in the texture of the examined muscle sections. Third, a novel method used with great success in the computer vision domain was presented, allowing the extraction of a high-level feature representation, by encoding the distribution of locally invariant texture descriptors. On the muscle-type recognition our method achieved an 87.07% classification rate, and on the task of gender recognition it surpassed state-of-the-art textural representations, reported in the literature in almost all the examined muscle sections.