目的：呼吸运动引起的内脏器官移位对图像引导放射治疗提出了重大挑战，特别是影响肝脏标志跟踪的准确性。
方法：解决这个问题，我们提出了一种用于长肝脏超声序列中的鲁棒标志跟踪的自监督方法。我们的方法利用了基于暹罗的上下文感知相关滤波器网络，通过使用前向跟踪和反向验证之间的一致性损失进行训练。通过有效利用标记和未标记的肝脏超声图像，我们的模型,暹罗-CCF,通过上下文感知相关滤波器减轻斑点噪声和伪影对超声图像跟踪的影响。此外,模板补丁特征的融合策略有助于跟踪器获得丰富的点标志周围的外观信息。
结果：Siam-CCF在118.6fps的帧速率下实现了0.79±0.83mm的平均跟踪误差，在公开的MICCAI2015挑战肝脏超声跟踪（CLUST2015）2D数据集上表现出卓越的速度-准确性权衡。此表演在CLUST20152D点地标跟踪任务中获得了第五名。
结论：大量实验验证了我们提出的方法的有效性，在此提交时，将其确立为CLUST2015在线排行榜上表现最好的技术之一。
OBJECTIVE: Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy.
METHODS: Addressing this concern, we propose a self-supervised method for robust landmark tracking in long liver ultrasound sequences. Our approach leverages a Siamese-based context-aware correlation filter network, trained by using the consistency loss between forward tracking and back verification. By effectively utilizing both labeled and unlabeled liver ultrasound images, our model, Siam-CCF , mitigates the impact of speckle noise and artifacts on ultrasonic image tracking by a context-aware correlation filter. Additionally, a fusion strategy for template patch feature helps the tracker to obtain rich appearance information around the point-landmark.
RESULTS: Siam-CCF achieves a mean tracking error of 0.79 ± 0.83 mm at a frame rate of 118.6 fps, exhibiting a superior speed-accuracy trade-off on the public MICCAI 2015 Challenge on Liver Ultrasound Tracking (CLUST2015) 2D dataset. This performance won the 5th place on the CLUST2015 2D point-landmark tracking task.
CONCLUSIONS: Extensive experiments validate the effectiveness of our proposed approach, establishing it as one of the top-performing techniques on the CLUST2015 online leaderboard at the time of this submission.
