Background:In this research, imaging techniques such as CT and X-ray are used to locate important muscles in the shoulders and legs. Athletes who participate in sports that require running, jumping, or throwing are more likely to get injuries such as sprains, strains, tendinitis, fractures, and dislocations. One proposed automated technique has the overarching goal of enhancing recognition. Objective:This study aims to determine how to recognize the major muscles in the shoulder and leg utilizing X-ray CT images as its primary diagnostic tool. Methods:Using a shape model, discovering landmarks, and generating a form model are the steps necessary to identify injuries in key shoulder and leg muscles. The method also involves identifying injuries in significant abdominal muscles. The use of adversarial deep learning, and more specifically Deep-Injury Region Identification, can improve the ability to identify damaged muscle in X-ray and CT images. Results:Applying the proposed diagnostic model to 150 sets of CT images, the study results show that Jaccard similarity coefficient (JSC) rate for the procedure is 0.724, the repeatability is 0.678, and the accuracy is 94.9% respectively. Conclusion:The study results demonstrate feasibility of using adversarial deep learning and deep-injury region identification to automatically detect severe muscle injuries in the shoulder and leg, which can enhance the identification and diagnosis of injuries in athletes, especially for those who compete in sports that include running, jumping, and throwing.

中文翻译：

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X 射线 CT 图像上运动员主要肌肉损伤的自动识别 1

背景：在这项研究中，CT 和 X 射线等成像技术用于定位肩部和腿部的重要肌肉。参加需要跑、跳或投掷的运动的运动员更容易受到扭伤、拉伤、肌腱炎、骨折和脱臼等伤害。一项提出的自动化技术的总体目标是增强识别。目的：本研究旨在确定如何利用 X 射线 CT 图像作为主要诊断工具来识别肩部和腿部的主要肌肉。方法：使用形状模型、发现标志并生成形状模型是识别肩部和腿部关键肌肉损伤的必要步骤。该方法还涉及识别重要腹部肌肉的损伤。使用对抗性深度学习，更具体地说是深度损伤区域识别，可以提高识别 X 射线和 CT 图像中受损肌肉的能力。结果：将所提出的诊断模型应用到150组CT图像中，研究结果表明该过程的Jaccard相似系数（JSC）率为0.724，重复性为0.678，准确率为94.9%。结论：研究结果证明了利用对抗性深度学习和深度损伤区域识别自动检测肩部和腿部严重肌肉损伤的可行性，可以增强运动员损伤的识别和诊断，特别是对于那些参加运动项目的运动员包括跑、跳、投掷。