Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study.

Journal: IEEE transactions on bio-medical engineering
Published Date: May 19, 2023

Abstract

OBJECTIVE: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks.

Authors

  • Matthew Ng
    Sunnybrook Research Institute, University of Toronto, Toronto M4N 3M5, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada.
  • Fumin Guo
    School of Mechatronics Engineering, Nanchang University, Nanchang 330031, China.
  • Labonny Biswas
  • Steffen E Petersen
    Cardiac Imaging Department, Barts Heart Centre, St Bartholomew's Hospital, London, UK; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK.
  • Stefan K Piechnik
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
  • Stefan Neubauer
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
  • Graham Wright

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