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摘要
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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. |