UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Nonlinear Markov Random Fields Learned via Backpropagation

Brudfors, M; Balbastre, Y; Ashburner, J; (2019) Nonlinear Markov Random Fields Learned via Backpropagation. In: Chung, A.C.S. and Gee, J.C. and Yushkevich, P.A. and Bao, S., (eds.) Proceedings of the 26th International Conference on Information Processing in Medical Imaging (IPMI 2019). (pp. pp. 805-817). Springer: Cham, Switzerland. Green open access

[img]
Preview
Text
1902.10747v2.pdf - Accepted version

Download (1MB) | Preview

Abstract

Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on mixture models are still used in practice to parcellate brains. To bridge the gap between the two, in this paper we propose a marriage between a probabilistic generative model, which has been shown to be robust to variability among magnetic resonance (MR) images acquired via different imaging protocols, and a CNN. The link is in the prior distribution over the unknown tissue classes, which are classically modelled using a Markov random field. In this work we model the interactions among neighbouring pixels by a type of recurrent CNN, which can encode more complex spatial interactions. We validate our proposed model on publicly available MR data, from different centres, and show that it generalises across imaging protocols. This result demonstrates a successful and principled inclusion of a CNN in a generative model, which in turn could be adapted by any probabilistic generative approach for image segmentation.

Type: Proceedings paper
Title: Nonlinear Markov Random Fields Learned via Backpropagation
Event: 26th International Conference on Information Processing in Medical Imaging (IPMI 2019), 2-7 June 2019, Hong Kong, China
ISBN-13: 978-3-030-20351-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-20351-1_63
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: cs.CV, cs.LG, stat.ML
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 Chemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10076941
Downloads since deposit
7Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item