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Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound

  • Nicoló SavioliEmail author
  • Silvia VisentinEmail author
  • Erich CosmiEmail author
  • Enrico GrisanEmail author
  • Pablo LamataEmail author
  • Giovanni MontanaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11140)

Abstract

The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional layer for the extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for enforcing the temporal coherence across video frames and exploiting the temporal redundancy of a signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. We present experimental evidence suggesting that the proposed architecture can reach an accuracy substantially superior to previously proposed methods, providing an average reduction of the mean squared error from \(0.31\,\mathrm{mm}^2\) (state-of-art) to \(0.09\,\mathrm{mm}^2\), and a relative error reduction from \(8.1\%\) to \(5.3\%\). The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use.

Keywords

Cardiac imaging Diameter Ultrasound Convolutional networks Fetal imaging GRU CyclicLoss 

Notes

Acknowledgement

This work was supported by the Wellcome/EPSRC Centre for Medical Engineering at Kings College London (WT 203148/Z/16/Z). Dr. Lamata holds a Wellcome Trust Senior Research Fellowship (grant n.209450/Z/17/Z).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringKings College LondonLondonUK
  2. 2.Department of Woman and Child HealthUniversity Hospital of PadovaPaduaItaly
  3. 3.Department of Information EngineeringUniversity of PadovaPaduaItaly
  4. 4.WMG, University of WarwickCoventryUK

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