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Optical Flow Networks for Heartbeat Estimation in 4D Ultrasound Images

Published:24 September 2021Publication History

ABSTRACT

Congenital heart defects is one of the most common neonatal diseases and has a very low survival rate. The fetal heart is generally smaller and possesses a faster than normal beating rate, thus making medical diagnosis difficult. The efficiency and accuracy of diagnosis of congenital heart disease can be improved by computer-aided diagnostic methods. Optical flow is a robust algorithm for object recognition and motion detection, and has potential in early detection of congenital heart defects. In this paper, an end-to-end deep learning system is proposed for obtaining the optical flow information from 4D fetal cardiac ultrasound images. The optical flow network model is trained by using gradients of image sequences obtained from a virtual data set. Subsequently, the trained model is used to detect the cardiac motion. Experimental results and performance evaluation demonstrate the effectiveness of the proposed network. Apart from the efficacy of the proposed method, a visualization of the fetal cardiac motion using pseudo-color is provided. It is envisaged that the proposed method can be used in clinical applications requiring automatic detection of congenital fetal heart defects.

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  • Published in

    cover image ACM Other conferences
    ICCAI '21: Proceedings of the 2021 7th International Conference on Computing and Artificial Intelligence
    April 2021
    498 pages
    ISBN:9781450389501
    DOI:10.1145/3467707

    Copyright © 2021 ACM

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    Publication History

    • Published: 24 September 2021

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