Echocardiographic Phase Detection Using Neural NetworksDownload PDF

Published: 11 May 2021, Last Modified: 16 May 2023MIDL 2021 PosterReaders: Everyone
Keywords: Echocardiography, Cardiac imaging, Deep learning, Phase detection.
TL;DR: Multibeat echocardiographic phase detection using neural networks for videos of arbitrary length.
Abstract: Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is essential when measuring cardiac function. Manual selection by human experts is challenging and error prone. This paper presents a deep neural network trained and tested on multi-centre patient data for accurate phase detection in apical four-chamber videos of arbitrary length, spanning several heartbeats, with performance indistinguishable from that of human experts.
Paper Type: both
Primary Subject Area: Detection and Diagnosis
Secondary Subject Area: Transfer Learning and Domain Adaptation
Paper Status: based on accepted/submitted journal paper
Source Code Url:
Data Set Url:
Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
4 Replies