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Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

  • Ilkay Oksuz
  • Bram Ruijsink
  • Esther Puyol-Antón
  • Aurelien Bustin
  • Gastao Cruz
  • Claudia Prieto
  • Daniel Rueckert
  • Julia A. Schnabel
  • Andrew P. King
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11070)

Abstract

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1 ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.

Keywords

Cardiac MR Image quality assessment Motion artefacts UK Biobank 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). This research has been conducted using the UK Biobank Resource under Application Number 17806. The GPU used in this research was generously donated by the NVIDIA Corporation.

Supplementary material

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ilkay Oksuz
    • 1
  • Bram Ruijsink
    • 1
    • 2
  • Esther Puyol-Antón
    • 1
  • Aurelien Bustin
    • 1
  • Gastao Cruz
    • 1
  • Claudia Prieto
    • 1
  • Daniel Rueckert
    • 3
  • Julia A. Schnabel
    • 1
  • Andrew P. King
    • 1
  1. 1.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK
  2. 2.Guy’s and St Thomas’ Hospital NHS Foundation TrustLondonUK
  3. 3.Biomedical Image Analysis GroupImperial College LondonLondonUK

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