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.
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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 > 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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