Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks
This work addresses the challenge of reliable motion tracking in noisy, low-resolution ultrasound imaging for cardiac function assessment, offering an incremental improvement as the first end-to-end unsupervised deep learning method for this specific domain.
The paper tackled the problem of cardiac motion tracking from echocardiography by proposing an unsupervised optical flow network with spatial reconstruction and temporal-consistency losses, achieving superior accuracy and running speed over existing methods on a synthetic 4D dataset.
Cardiac motion tracking from echocardiography can be used to estimate and quantify myocardial motion within a cardiac cycle. It is a cost-efficient and effective approach for assessing myocardial function. However, ultrasound imaging has the inherent characteristics of spatially low resolution and temporally random noise, which leads to difficulties in obtaining reliable annotation. Thus it is difficult to perform supervised learning for motion tracking. In addition, there is no end-to-end unsupervised method currently in the literature. This paper presents a motion tracking method where unsupervised optical flow networks are designed with spatial reconstruction loss and temporal-consistency loss. Our proposed loss functions make use of the pair-wise and temporal correlation to estimate cardiac motion from noisy background. Experiments using a synthetic 4D echocardiography dataset has shown the effectiveness of our approach, and its superiority over existing methods on both accuracy and running speed. To the best of our knowledge, this is the first work performed that uses unsupervised end-to-end deep learning optical flow network for 4D cardiac motion tracking.