MNTDeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions Abstract   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 timeconsuming. 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

Cardiac Left Ventricular Ultrasound Image Sequence Recognition and Tracking A novel approach combining faster regionbased convolutional neural network (Faster RCNN) with active shape model is proposed to automatically recognize and track cardiac ultrasound left ventricular image sequences in this work. Because the shape and appearance of the left ventricle vary considerably between adjacent images, conventional methods cannot effectively identify its position and then accurately track them. We propose a novel method, incorporating Faster RCNN and active shape model, to automatically recognize, segment and track the cardiac ultrasound left ventricular image sequences. Compared with four stateoftheart approaches, the proposed method has most of better performance in SorensenDice coefficient, mean absolute deviation, and Hausdorff distance.