EchoTracker: Advancing Myocardial Point Tracking in Echocardiography

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.

Architecture

EchoTracker includes two stages as shown in the figure, “Initialization“ and “Iterative reinforcement“. The approach follows a two-fold coarse-to-fine strategy inspired by TAPIR. In the initial stage, trajectories are initialized based on the coarse resolution of the feature maps using a coarse network. Subsequently, in the second stage, the trajectories are iteratively refined using fine-grained feature maps by a fine network, thus constituting a two-fold coarse-to-fine approach. This technique not only speeds up computation but also prevents the loss of important information due to downsampling. Although the networks in both stages estimate trajectories independently, they exploit point locations from the first frame to maintain spatial correlation and estimate coherent trajectories. Additionally, frame flow, representing the difference between consecutive frames, is naively passed to the model to make it aware of global appearance changes. The model can run on ultrasound sequences of any length and with any number of query points, depending on available memory.

EchoTracker Performance

Technical Results

Method	< δ¹ ↑	< δ² ↑	< δ⁴ ↑	< δ⁸ ↑	< δ¹⁶ ↑	< δ^avg ↑	MTE ↓	AIT(s) ↓
TAPIR	14	34	67	92	99	61	3.64	0.62
PIPs++	15	36	70	94	100	63	3.28	0.42
CoTracker	19	42	74	95	100	66	3.02	1.34
EchoTracker (ours)	19	43	76	96	100	67	2.86	0.24

Performance on a test-retest dataset compared to state-of-the-art methods.

EchoTracker accurately estimates trajectories given the query points on the myocardial ventricle wall.

Clinical Results

Method	Reference			Test-retest
Method	μ	σ ↓	MAD ↓	μ	σ ↓	MAD ↓
c-PWC-Net-60A	1.85	2.73	N/A
us2ai	0.68	2.52	2.0
EchoPWCNet	-1.4	1.9	1.8	0.0	1.9	1.6

PIPs++	-1.21	1.95	1.76	0.11	1.62	1.28
CoTracker	-0.82	2.40	1.98	-0.11	2.47	1.96
EchoTracker (ours)	-0.13	1.78	1.36	-0.13	1.55	1.21

Clinical results for GLS calculations compared to reference measurements and in a test-retest scenario.

Citation\BibTeX

Please cite our work if you utilize the paper/code/project in anyway.

@InProceedings{azad2024echo,
      author="Azad, Md Abulkalam
      and Chernyshov, Artem
      and Nyberg, John
      and Tveten, Ingrid
      and Lovstakken, Lasse
      and Dalen, H{\aa}vard
      and Grenne, Bj{\o}rnar
      and {\O}stvik, Andreas",
      title="EchoTracker: Advancing Myocardial Point Tracking in Echocardiography",
      booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024",
      year="2024",
      volume="IV",
      publisher="Springer Nature Switzerland",
      pages="645--655"
      }