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Variational inference for medical image segmentation

Blaiotta, C; Cardoso, MJ; Ashburner, J; (2016) Variational inference for medical image segmentation. Computer Vision and Image Understanding , 151 pp. 14-28. 10.1016/j.cviu.2016.04.004. Green open access

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Abstract

Variational inference techniques are powerful methods for learning probabilistic models and provide significant advantages over maximum likelihood (ML) or maximum a posteriori (MAP) approaches. Nevertheless they have not yet been fully exploited for image processing applications. In this paper we present a variational Bayes (VB) approach for image segmentation. We aim to show that VB provides a framework for generalising existing segmentation algorithms that rely on an expectation–maximisation formulation, while increasing their robustness and computational stability. We also show how optimal model complexity can be automatically determined in a variational setting, as opposed to ML frameworks which are intrinsically prone to overfitting. Finally, we demonstrate how suitable intensity priors, that can be used in combination with the presented algorithm, can be learned from large imaging data sets by adopting an empirical Bayes approach.

Type: Article
Title: Variational inference for medical image segmentation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.cviu.2016.04.004
Publisher version: http://doi.org/10.1016/j.cviu.2016.04.004
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.
Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, Image segmentation, Bayesian inference, Variational Bayes, Neuroimaging, MRI, BRAIN MR-IMAGES, AUTOMATIC SEGMENTATION, JOINT SEGMENTATION, MIXTURE-MODELS, REGISTRATION, VOLUMETRY, ALGORITHM, FRAMEWORK, CLASSIFICATION, FIELD
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 Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
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 Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1485935
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