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Proceedings Paper

Unsupervised motion tracking of left ventricle in echocardiography
Author(s): Shawn S. Ahn; Kevinminh Ta; Allen Lu; John C. Stendahl; Albert J. Sinusas; James S. Duncan
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Paper Abstract

Accurate motion tracking of the left ventricle is critical in detecting wall motion abnormalities in the heart after an injury such as a myocardial infarction. We propose an unsupervised motion tracking framework with physiological constraints to learn dense displacement fields between sequential pairs of 2-D B-mode echocardiography images. Current deep-learning motion-tracking algorithms require large amounts of data to provide ground-truth, which is difficult to obtain for in vivo datasets (such as patient data and animal studies), or are unsuccessful in tracking motion between echocardiographic images due to inherent ultrasound properties (such as low signal-to-noise ratio and various image artifacts). We design a U-Net inspired convolutional neural network that uses manually traced segmentations as a guide to learn displacement estimations between a source and target image without ground- truth displacement fields by minimizing the difference between a transformed source frame and the original target frame. We then penalize divergence in the displacement field in order to enforce incompressibility within the left ventricle. We demonstrate the performance of our model on synthetic and in vivo canine 2-D echocardiography datasets by comparing it against a non-rigid registration algorithm and a shape-tracking algorithm. Our results show favorable performance of our model against both methods.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 113190Z (16 March 2020); doi: 10.1117/12.2549572
Show Author Affiliations
Shawn S. Ahn, Yale Univ. (United States)
Kevinminh Ta, Yale Univ. (United States)
Allen Lu, EchoNous Inc. (United States)
John C. Stendahl, Yale Univ. (United States)
Albert J. Sinusas, Yale Univ. (United States)
James S. Duncan, Yale Univ. (United States)

Published in SPIE Proceedings Vol. 11319:
Medical Imaging 2020: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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