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。参加需要跑步的运动的运动员,跳跃,或者投掷更容易受伤,比如扭伤,菌株,肌腱炎,骨折,和错位。一种提出的自动化技术具有增强识别的总体目标。
目的:本研究旨在确定如何利用X射线CT图像作为其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,发现地标,和生成的形式模型是必要的步骤,以确定关键的肩部和腿部肌肉损伤。该方法还涉及识别重要腹部肌肉的损伤。对抗性深度学习的使用,更具体地说,深度损伤区域识别,能提高X线和CT图像对受损肌肉的识别能力。
结果:将所提出的诊断模型应用于150组CT图像,研究结果表明,该方法的Jaccard相似系数(JSC)为0.724,重复性为0.678,准确率为94.9%。
结论:研究结果表明,使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。这可以增强运动员受伤的识别和诊断,特别是对于那些参加包括跑步在内的体育运动的人来说,跳跃,扔。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.