Analysis of Advanced Siamese Neural Networks for Motion Tracking of Sonography of Carotid Arteries

The Siamese Tracker (ST) for tracking objects of interest in Ultrasound (US) images does not incorporate video specific cues and assumes a fixed template of the reference block. Recently, a more advanced version of ST, Correlation Filter Network (CFNet), which overcomes the problems of ST, has been used for tracking in US images. In this study, we demonstrate how the basic CFNet can be made computationally more efficient by reducing the number of layers in its feature extraction network. We further show that due to the unique architecture of the CFNet, this strategy does not affect the performance of the baseline CFNet considerably. Our methodology was evaluated on 10 random sequences from the publicly available carotid artery dataset. CFNet obtained a 35.7% improvement in the average localization error over the basic ST, thus demonstrating that it is a practical and robust tracking algorithm for tracking objects in US images.