Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2173-2176. doi: 10.1109/EMBC48229.2022.9871782.
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.
暹罗跟踪器（ST）用于跟踪超声（US）图像中的感兴趣对象，它不包含视频特定线索，并假设参考块的固定模板。最近，一种更先进的 ST 版本——相关滤波器网络（CFNet）已被用于 US 图像的跟踪，该版本克服了 ST 的问题。在本研究中，我们展示了如何通过减少其特征提取网络中的层数，使基本 CFNet 在计算上更加高效。我们进一步表明，由于 CFNet 的独特架构，该策略不会对基准 CFNet 的性能产生重大影响。我们的方法在公开的颈动脉数据集的 10 个随机序列上进行了评估。CFNet 使平均定位误差相对于基本 ST 提高了 35.7%，这表明它是一种实用且强大的跟踪算法，可用于跟踪 US 图像中的对象。
