bibtext: @InProceedings{ Aza_EchoTracker_MICCAI2024,
                   author = { Azad, Md Abulkalam and Chernyshov, Artem and Nyberg, John and Tveten, Ingrid and Lovstakken, Lasse and Dalen, Håvard and Grenne, Bjørnar and Østvik, Andreas },
                   title = { { EchoTracker: Advancing Myocardial Point Tracking in Echocardiography } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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//.
title: EchoTracker: Advancing Myocardial Point Tracking in Echocardiography
authors: Azad, Md Abulkalam Chernyshov, Artem Nyberg, John Tveten, Ingrid Lovstakken, Lasse Dalen, Håvard Grenne, Bjørnar Østvik, Andreas