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Background: Synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome is a rare autoimmune disease characterized by skin or osteoarticular damage. SAPHO syndrome is often misdiagnosed or missed diagnosis due to lack of overall understanding of the disease by clinicians. Purpose: To analyze the clinical symptoms and imaging features of six Han patients with SAPHO syndrome in order to provide reference for doctors to diagnose SAPHO syndrome. Material and Methods: This study retrospectively analyzed the clinical data of six Han patients with SAPHO syndrome. Results: All six Han patients with SAPHO syndrome had severe acne or pustulosis of the hands and feet, and all of them had osteoarticular damage, including five cases involving the sternoclavicular joint. Some patients showed a specific and typical "bull's head" sign on 99mTc-labeled methylene diphosphonate bone imaging. Among the six patients recruited, there was one thoracic vertebra, one cervical vertebra, one sacroiliac joint, and one peripheral joint involvement. Two patients had limited activity due to severe osteoarticular damage. Conclusion: Due to the atypical clinical symptoms of SAPHO syndrome, most patients will experience a tortuous and long diagnostic process, while a correct understanding and timely intervention of SAPHO syndrome are essential to improve the prognosis of patients. [ABSTRACT FROM AUTHOR]

Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for understanding the mechanics and pathological conditions of the muscle–tendon unit. However, the lack of reliable and efficient identification of MTJ due to poor image quality and boundary ambiguity restricts its application in motion analysis. In recent years, with the rapid development of deep learning, the region-based convolution neural network (RCNN) has shown great potential in the field of simultaneous objection detection and instance segmentation in medical images. This article proposes a region-adaptive network (RAN) to localize MTJ region and to segment it in a single shot. Our model learns about the salient information of MTJ with the help of a composite architecture. Herein, a region-based multitask learning network explores the region containing MTJ, while a parallel end-to-end U-shaped path extracts the MTJ structure from the adaptively selected region for combating data imbalance and boundary ambiguity. By demonstrating the ultrasound images of the gastrocnemius, we showed that the RAN achieves superior segmentation performance when compared with the state-of-the-art Mask RCNN method with an average Dice score of 80.1%. Our proposed method is robust and reliable for advanced muscle and tendon function examinations obtained by ultrasound imaging. [ABSTRACT FROM AUTHOR]

Displacement of the myotendinous junction (MTJ) obtained by ultrasound imaging is crucial to quantify the interactive length changes of muscles and tendons for understanding the mechanics and pathological conditions of the muscle-tendon unit during motion. However, the lack of a reliable automatic measurement method restricts its application in human motion analysis. This paper presents an automated measurement of MTJ displacement using prior knowledge on tendinous tissues and MTJ, precluding the influence of nontendinous components on the estimation of MTJ displacement. It is based on the perception of tendinous features from musculoskeletal ultrasound images using Radon transform and thresholding methods, with information about the symmetric measures obtained from phase congruency. The displacement of MTJ is achieved by tracking manually marked points on tendinous tissues with the Lucas-Kanade optical flow algorithm applied over the segmented MTJ region. The performance of this method was evaluated on ultrasound images of the gastrocnemius obtained from 10 healthy subjects (26.0±2.9 years of age). Waveform similarity between the manual and automatic measurements was assessed by calculating the overall similarity with the coefficient of multiple correlation (CMC). In vivo experiments demonstrated that MTJ tracking with the proposed method (CMC = 0.97±0.02) was more consistent with the manual measurements than existing optical flow tracking methods (CMC = 0.79±0.11). This study demonstrated that the proposed method was robust to the interference of nontendinous components, resulting in a more reliable measurement of MTJ displacement, which may facilitate further research and applications related to the architectural change of muscles and tendons. [ABSTRACT FROM AUTHOR]