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Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

Tanno, R; Worrall, DE; Ghosh, A; Kaden, E; Sotiropoulos, SN; Criminisi, A; Alexander, DC; (2017) Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution. In: Descoteaux, M and Maier-Hein, L and Franz, A and Jannin, P and Collins, D and Duchesne, S, (eds.) MICCAI 2017: 20th International Conference, Medical Image Computing and Computer Assisted Intervention: Proceedings, Part I. (pp. pp. 611-619). Springer International Publishing: Cham, Switzerland. Green open access

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Abstract

In this work, we investigate the value of uncertainty modeling 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.

Type: Proceedings paper
Title: Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
Event: MICCAI 2017, 20th International Conference, Medical Image Computing and Computer Assisted Intervention, 11-13 September 2017, Quebec City, Canada
ISBN-13: 9783319661810
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-66182-7_70
Publisher version: http://dx.doi.org/10.1007/978-3-319-66182-7_70
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1556163
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