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Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography by Dezaki, Fatemeh Taheri; Luong, Christina; Ginsberg, Tom; Rohling, Robert; Gin, Ken; Abolmaesumi, Purang; Tsang, Teresa is a scholarly article available to read on EtoBox.

What is Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography about?

In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergencies or point-of-care situations, acquiring an ECG is often not an option, hence motivating the need for alternative temporal synchronization methods. Here, we propose Echo-SyncNet, a self-supervised learning framework to synchronize various cross-sectional 2D echo series without any external input. The proposed framework takes advantage of both intra-view and inter-view self supervisions. The former relies on spatiotemporal patterns found between the frames of a single echo cine and the latter on the interdependencies between multiple cines. The combined supervisions are used to learn a feature-rich embedding space where multiple echo cines can be temporally synchronized. We evaluate the framework with multiple experiments: 1) Using data from 998 patients, Echo-SyncNet shows promising results for synchronizing Apical 2 chamber and Apical 4 chamber cardiac views; 2) Using data from 3070 patients, our experiments reveal that the learned representations of Echo-SyncNet outperform a supervised deep learning me

Author

Dezaki, Fatemeh Taheri; Luong, Christina; Ginsberg, Tom; Rohling, Robert; Gin, Ken; Abolmaesumi, Purang; Tsang, Teresa

Published

2021

Language

EN