发表机构
Keio University（庆应义塾大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种仅用常规RGB相机、结合分割预处理和震颤评分估计模块的无标记方法，实现对自由活动小鼠震颤严重程度的客观量化，与加速度计结果强相关。
AI 中文摘要
震颤是一种运动障碍，其特征是身体部位出现不自主的节律性振荡，并且是多种神经系统疾病（包括帕金森病和特发性震颤）的标志性症状。阐明其潜在机制在很大程度上依赖于小鼠模型，这些模型具有遗传可操作性和与人类神经回路的相关性。因此，这些模型对于研究震颤的病理生理学是不可或缺的。到目前为止，肌电图和加速度计已被用作定量观察小鼠震颤的方法。然而，这些方法存在若干缺点，例如成本高和设置复杂。特别是，侵入性手术植入设备会给动物带来显著压力。尽管基于RGB的方法提供了非侵入性和成本效益高的替代方案，但它们通常缺乏检测细微震颤所需的灵敏度。因此，本文通过仅使用常规RGB相机实现小鼠震颤严重程度估计来解决这些挑战。为了解决在小鼠自身也在运动时分离震颤相关振动的艰巨任务，我们的流程结合了基于分割的预处理以提取小鼠区域，以及一个震颤评分估计模块，该模块以高灵敏度捕获细微震颤。在实验中，我们使用非侵入性方法通过两台标准相机评估了不受约束的小鼠的震颤。结果表明，该方法与加速度计测量结果具有强相关性，并证实了该方法准确捕获了震颤的强度依赖性特征。项目页面可在以下网址获取：此https URL。
英文摘要
Tremor is a movement disorder characterized by involuntary, rhythmic oscillations of body parts and is a hallmark of several neurological conditions, including Parkinson's disease and essential tremor. Elucidating its underlying mechanisms relies heavily on mouse models, which offer genetic manipulability and translational relevance to human neural circuitry. Accordingly, these models are indispensable for studying tremor pathophysiology. So far, electromyography and accelerometers have been used as methods to quantitatively observe tremors in mice. However, these methods have several drawbacks, such as high costs and complex setups. In particular, the invasive surgical implantation of devices causes significant stress to the animals. Although RGB-based methods offer non-invasive and cost-effective alternatives, they often lack the sensitivity required to detect subtle tremors. Therefore, this paper addresses these challenges by achieving mouse tremor severity estimation using conventional RGB cameras only. To address the challenging task of isolating tremor-related vibrations while the mouse itself is also in motion, our pipeline incorporates segmentation-based pre-processing to extract the mouse region and a Tremor Score Estimation Module that captures subtle tremors with high sensitivity. In the experiments, we assessed tremors in unrestrained mice using a non-invasive method with two standard cameras. The results demonstrated a strong correlation with accelerometer measurements and confirmed that the method accurately captured the intensity-dependent characteristics of tremors. The project page is available at https://isogawalab.github.io/Video-based-Tremor-Analysis-Project/.
