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Deep Quantitative Liver Segmentation and Vessel Exclusion to Assist in Liver Assessment

  • Benjamin Irving
  • Chloe Hutton
  • Andrea Dennis
  • Sid Vikal
  • Marija Mavar
  • Matt Kelly
  • J. Michael Brady
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 723)

Abstract

Liver disease, especially Non-Alcoholic Fatty Liver Disease has reached high levels, and there is a need for non-invasive tests based on quantitative MRI to replace biopsy in order to better assess liver health. An automated quantitative liver segmentation approach is required to automate these tests and in this work we propose a fully convolutional framework with a novel objective function for quantitative liver segmentation. The method has (to date) been tested on quantitative T1 maps generated from the UK Biobank study. We obtained extremely encouraging results on an unseen test set with a Dice score of 0.95, and Sensitivity 0.98 and Specificity 0.99.

Keywords

Segmentation MRI Liver Convolutional neural networks Deep learning 

Notes

Acknowledgements

This research has been conducted using the UK Biobank Resource under application 9914.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Benjamin Irving
    • 1
  • Chloe Hutton
    • 1
  • Andrea Dennis
    • 1
  • Sid Vikal
    • 1
  • Marija Mavar
    • 1
  • Matt Kelly
    • 1
  • J. Michael Brady
    • 1
    • 2
  1. 1.Perspectum DiagnosticsOxfordUK
  2. 2.Department of OncologyUniversity of OxfordOxfordUK

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