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.Comment: Accepted for the international conference on Information Processing
in Medical Imaging (IPMI) 2019, camera ready versio