{Reference Type}: Journal Article
{Title}: Self-supervised context-aware correlation filter for robust landmark tracking in liver ultrasound sequences.
{Author}: Ma L;Wang J;Gong S;Lan L;Geng L;Wang S;Feng X;
{Journal}: Biomed Tech (Berl)
{Volume}: 0
{Issue}: 0
{Year}: 2024 Feb 7
{Factor}: 1.426
{DOI}: 10.1515/bmt-2022-0489
{Abstract}: <B>OBJECTIVE: </B>Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.