Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4095-4098. doi: 10.1109/EMBC46164.2021.9631026.
Due to their great success in learning a universal object similarity metric, Siamese Trackers have been adopted for motion tracking a Region of Interest (ROI) in Ultrasound (US) image sequences. However, these Fully Convolutional Siamese networks (SiamFC) offer no online adaptation of the network and fail to take cues from the input sequence. The more recent Correlation Filter Networks (CFNet) solve this problem by learning the reference template online using a Correlation Filter layer. In this work, we use the CFNet as our backbone model and propose an advanced tracking algorithm (Seq-CFNet) for tracking an ROI in US sequences by constructing a sequential cascade of two identical CFNet. The cascade with CFNet is novel and offers practical benefits in tracking accuracy. Our method is evaluated on 10 different sequences of a Carotid Artery (CA) dataset to track the transverse section of the carotid artery. Results show that Seq-CFNet obtains better Root Mean Square Error (RMSE) values than the baseline CFNet as well as SiamFC, without significantly compromising the speed.
由于在学习通用目标相似性度量方面取得了巨大成功，孪生跟踪器已被用于在超声 (US) 图像序列中跟踪感兴趣区域 (ROI)。然而，这些全卷积孪生网络 (SiamFC) 没有提供网络的在线自适应功能，也无法从输入序列中获取线索。最近的相关滤波网络 (CFNet) 通过使用相关滤波层在线学习参考模板来解决这个问题。在这项工作中，我们使用 CFNet 作为我们的骨干模型，并通过构建两个相同的 CFNet 的顺序级联提出了一种用于跟踪 US 序列中 ROI 的先进跟踪算法 (Seq-CFNet)。CFNet 级联是新颖的，并在跟踪精度方面提供了实际的好处。我们的方法在颈动脉 (CA) 数据集的 10 个不同序列上进行了评估，以跟踪颈动脉的横截面积。结果表明，Seq-CFNet 获得了比基线 CFNet 以及 SiamFC 更好的均方根误差 (RMSE) 值，而速度没有明显下降。
