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Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms

  • Fatemeh Taheri Dezaki
  • Neeraj Dhungel
  • Amir H. Abdi
  • Christina Luong
  • Teresa Tsang
  • John Jue
  • Ken Gin
  • Dale Hawley
  • Robert Rohling
  • Purang Abolmaesumi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

Characterisation of cardiac cycle phase in echocardiography data is a necessary preprocessing step for developing automated systems that measure various cardiac parameters. Accurate characterisation is challenging, due to differences in appearance of the cardiac anatomy and the variability of heart rate in individuals. Here, we present a method for automatic recognition of cardiac cycle phase from echocardiograms by using a new deep neural networks architecture. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual echocardiogram frames, with recurrent neural networks (RNNs), which model the temporal dependencies between sequential frames. We demonstrate that such new architecture produces results that outperform baseline architecture for the automatic characterisation of cardiac cycle phase in large datasets of echocardiograms containing different levels of pathological conditions.

Keywords

Deep residual neural networks Recurrent neural networks Long short term memory Echocardiograms Frame identification 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fatemeh Taheri Dezaki
    • 1
  • Neeraj Dhungel
    • 1
  • Amir H. Abdi
    • 1
  • Christina Luong
    • 2
  • Teresa Tsang
    • 2
  • John Jue
    • 2
  • Ken Gin
    • 2
  • Dale Hawley
    • 2
  • Robert Rohling
    • 1
  • Purang Abolmaesumi
    • 1
  1. 1.The University of British ColumbiaVancouverCanada
  2. 2.Vancouver General Hospital’s Cardiology LaboratoryVancouverCanada

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