Echocardiographic Phase Detection Using Neural NetworksDownload PDF

Mar 17, 2021 (edited Jun 07, 2021)MIDL 2021 Conference Short SubmissionReaders: 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: https://github.com/intsav/EchoPhaseDetection
  • Data Set Url: https://intsav.github.io/ https://echonet.github.io/dynamic/
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