前臂肌肉的自动细分:与手部功能,肌肉体积和肌内脂肪的临床关联
Joel Fundaun1, Valeria Oliva1,2, Sandrine Bédard1,3
1Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine Stanford University School of Medicine Stanford California USA.
概括
自动化MRI细分精确评估前臂肌肉健康状况,将肌肉体积与握力联系起来. 这种计算机视觉方法有助于评估影响手部功能的条件.
科学领域:
- 生物医学工程 生物医学工程
- 放射学 放射学是一门学科.
- 计算机视觉 计算机视觉
背景情况:
- 手的功能对于日常活动至关重要,在肌肉骨和神经系统疾病的早期经常受到损害.
- 磁共振成像 (MRI) 的肌肉健康标记与功能能力相关,但传统的评估需要大量的时间.
- 使用计算机视觉的自动细分为有效的肌肉健康评估提供了潜在的解决方案.
研究的目的:
- 开发和验证计算机视觉模型,用于使用MRI进行自动化前臂肌肉细分.
- 调查MRI衍生的肌肉标记物 (体积,肌内脂肪) 和人口因素 (年龄,性别,BMI) 之间的关联.
- 探索肌肉标记物和功能性指标 (如握力和灵巧度) 之间的关系.
主要方法:
- 一个2D U-Net卷积神经网络被训练在手动细分的前臂肌肉从迪克森脂肪水MRI扫描.
- 该模型的准确性和可靠性使用Sørensen-Dice指数和类内相关系数 (ICC) 进行了测试.
- 使用相关性和回归模型分析了肌肉指标,人口统计和手部功能之间的关联.
主要成果:
- 自动化细分模型表现出高准确度 (索伦森-迪斯指数0.85-0.89) 和可靠性 (ICCs0.75-0.99).
- 肌肉体积与BMI正相关,并且在男性中更大,无论年龄如何.
- 握力的强度与肌肉体积有很强的正相关性,而肌内脂肪与功能无关.
结论:
- 通过计算机视觉自动化前臂肌肉细分是准确和可靠的.
- 肌肉体积受到BMI和性别的影响,与握力密切相关,这表明它对评估手部功能的临床相关性.
- 这项技术可以有效评估肌肉健康状况,在治疗规划和功能结果监测方面有潜在的应用.
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