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Publication Detail
Bayesian image quality transfer with CNNs: Exploring uncertainty in dMRI super-resolution
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Tanno R, Worrall DE, Ghosh A, Kaden E, Sotiropoulos SN, Criminisi A, Alexander DC
  • Publication date:
    04/09/2017
  • Pagination:
    611, 619
  • Journal:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    10433 LNCS
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
© 2017, Springer International Publishing AG. In this work, we investigate the value of uncertainty modelling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.
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