Automated recognition of the major muscle injury in athletes on X-ray CT images1.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Wanping Jia, Guangyong Zhao
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引用次数: 0

Abstract

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.

在 X 射线 CT 图像上自动识别运动员的主要肌肉损伤1。
背景:在这项研究中,CT 和 X 光等成像技术被用来定位肩部和腿部的重要肌肉。参加需要跑步、跳跃或投掷的运动的运动员更容易受伤,如扭伤、拉伤、肌腱炎、骨折和脱臼。一项拟议的自动化技术的首要目标是提高识别能力: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、发现地标和生成形状模型是识别肩部和腿部主要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。使用对抗式深度学习,特别是深度损伤区域识别,可以提高识别 X 光和 CT 图像中受损肌肉的能力:将提出的诊断模型应用于 150 组 CT 图像,研究结果显示,该程序的 Jaccard 相似系数(JSC)为 0.724,重复性为 0.678,准确率为 94.9%:研究结果表明,利用对抗式深度学习和深度损伤区域识别技术自动检测肩部和腿部的重症肌肉损伤是可行的,这可以提高对运动员损伤的识别和诊断,尤其是对那些参加跑步、跳跃和投掷等运动项目的运动员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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