EchoTracker: Advancing Myocardial Point Tracking in Echocardiography
<|reference_start|>EchoTracker: Advancing Myocardial Point Tracking in Echocardiography: Tissue tracking in echocardiography is challenging due to the complex cardiac motion and the inherent nature of ultrasound acquisitions. Although optical flow methods are considered state-of-the-art (SOTA), they struggle with long-range tracking, noise occlusions, and drift throughout the cardiac cycle. Recently, novel learning-based point tracking techniques have been introduced to tackle some of these issues. In this paper, we build upon these techniques and introduce EchoTracker, a two-fold coarse-to-fine model that facilitates the tracking of queried points on a tissue surface across ultrasound image sequences. The architecture contains a preliminary coarse initialization of the trajectories, followed by reinforcement iterations based on fine-grained appearance changes. It is efficient, light, and can run on mid-range GPUs. Experiments demonstrate that the model outperforms SOTA methods, with an average position accuracy of 67% and a median trajectory error of 2.86 pixels. Furthermore, we show a relative improvement of 25% when using our model to calculate the global longitudinal strain (GLS) in a clinical test-retest dataset compared to other methods. This implies that learning-based point tracking can potentially improve performance and yield a higher diagnostic and prognostic value for clinical measurements than current techniques. Our source code is available at: https://github.com/riponazad/echotracker/.<|reference_end|>
@article{azad2024echotracker:,
    title={EchoTracker: Advancing Myocardial Point Tracking in Echocardiography},
    author={Md Abulkalam Azad, Artem Chernyshov, John Nyberg, Ingrid Tveten, Lasse
  Lovstakken, H{aa}vard Dalen, Bj{o}rnar Grenne and Andreas {O}stvik},
    journal={arXiv preprint arXiv:2405.08587},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08587},
    primaryClass={cs.CV cs.AI}
}