Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Comput Med Imaging Graph. 2020 Mar;80:101687. doi: 10.1016/j.compmedimag.2019.101687. Epub 2020 Jan 20.
Carpal tunnel syndrome (CTS) is a clinical disease that caused by the compression of median nerve within carpal tunnel. Traditional examining for CTS is electrodiagnostic (EDx), but the evaluation of EDx is more expensive and time-consuming. In the present day, ultrasound (US) image is used to clinical examining to make up the lack of nerve electrical inspection. The diagnostic criteria of US image for CTS are also defined in many researches. In this study, we propose a new tracking model with deep similarity learning for median nerve from CTS US images. Six wrist motions are defined in the clinical rehabilitation, and the proposed method can achieve accuracy more than 90 % for median nerve tracking. In the experiment, we discover some wrist motions, such as hook to full fist, the statistical information of median nerve tracking is more significant (P < 0.001). It means that some wrist motions are more easily to diagnose the problem of median nerve, and can be used as a basis for quick examining for CTS.
腕管综合征（CTS）是一种由腕管内正中神经受压引起的临床疾病。CTS 的传统检查方法是电诊断（EDx），但 EDx 的评估更昂贵且耗时。如今，超声（US）图像已用于临床检查以弥补神经电检查的不足。许多研究已经定义了用于 CTS 的 US 图像的诊断标准。在这项研究中，我们提出了一种新的基于深度学习的相似性跟踪模型，用于从 CTS US 图像中跟踪正中神经。在临床康复中定义了六种手腕运动，所提出的方法可以实现超过 90%的正中神经跟踪准确性。在实验中，我们发现了一些手腕运动，例如钩到完全握拳，正中神经跟踪的统计信息更显著（P < 0.001）。这意味着一些手腕运动更容易诊断正中神经的问题，并可以作为 CTS 快速检查的依据。
