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Left-Ventricle Quantification Using Residual U-Net

  • Eric Kerfoot
  • James Clough
  • Ilkay OksuzEmail author
  • Jack Lee
  • Andrew P. King
  • Julia A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11395)

Abstract

Estimating dimensional measurements of the left ventricle provides diagnostic values which can be used to assess cardiac health and identify certain pathologies. In this paper we describe our methodology of calculating measurements from left ventricle segmentations automatically generated using deep learning. We use a U-net convolutional neural network architecture built from residual units to segment the left ventricle and then process these segmentations to estimate the area of the cavity and myocardium, the dimensions of the cavity, and the thickness of the myocardium. Determining if an image is part of the diastolic or systolic portion of the cardiac cycle is done by analysing the cavity volume. The quality of our results are dependent on our training regime where we have generated a large derivative dataset by augmenting the original images with free-form deformations. Our expanded training set, in conjunction with simple affine image transforms, creates a sufficiently large training population to prevent over-fitting of the network while still creating an accurate and robust segmentation network. Assessing our method on the STACOM18 LVQuan challenge dataset we find that it significantly outperforms the previously published state-of-the-art on a 5-fold validation all tasks considered.

Keywords

Cardiac MR Cardiac quantification Convolutional neural networks 

Notes

Acknowledgments

This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z). The GPUs used in this research was generously donated by the NVIDIA Corporation.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eric Kerfoot
    • 1
  • James Clough
    • 1
  • Ilkay Oksuz
    • 1
    Email author
  • Jack Lee
    • 1
  • Andrew P. King
    • 1
  • Julia A. Schnabel
    • 1
  1. 1.School of Biomedical Engineering & Imaging SciencesKing’s College LondonLondonUK

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