  Predicting the final ischaemic stroke lesion provides crucial information
regarding the volume of salvageable hypoperfused tissue, which helps physicians
in the difficult decision-making process of treatment planning and
intervention. Treatment selection is influenced by clinical diagnosis, which
requires delineating the stroke lesion, as well as characterising cerebral
blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting
the final stroke lesion is an intricate task, due to the variability in lesion
size, shape, location and the underlying cerebral haemodynamic processes that
occur after the ischaemic stroke takes place. Moreover, since elapsed time
between stroke and treatment is related to the loss of brain tissue, assessing
and predicting the final stroke lesion needs to be performed in a short period
of time, which makes the task even more complex. Therefore, there is a need for
automatic methods that predict the final stroke lesion and support physicians
in the treatment decision process. We propose a fully automatic deep learning
method based on unsupervised and supervised learning to predict the final
stroke lesion after 90 days. Our aim is to predict the final stroke lesion
location and extent, taking into account the underlying cerebral blood flow
dynamics that can influence the prediction. To achieve this, we propose a
two-branch Restricted Boltzmann Machine, which provides specialized data-driven
features from different sets of standard parametric Magnetic Resonance Imaging
maps. These data-driven feature maps are then combined with the parametric
Magnetic Resonance Imaging maps, and fed to a Convolutional and Recurrent
Neural Network architecture. We evaluated our proposal on the publicly
available ISLES 2017 testing dataset, reaching a Dice score of 0.38, Hausdorff
Distance of 29.21 mm, and Average Symmetric Surface Distance of 5.52 mm.
