  Reaction-diffusion models have been proposed for decades to capture the
growth of gliomas, the most common primary brain tumours. However, severe
limitations regarding the estimation of the initial conditions and parameter
values of such models have restrained their clinical use as a personalised
tool. In this work, we investigate the ability of deep convolutional neural
networks (DCNNs) to address the pitfalls commonly encountered in the field.
Based on 1,200 synthetic tumours grown over real brain geometries derived from
magnetic resonance (MR) data of 6 healthy subjects, we demonstrate the ability
of DCNNs to reconstruct a whole tumour cell density distribution from only two
imaging contours at a single time point. With an additional imaging contour
extracted at a prior time point, we also demonstrate the ability of DCNNs to
accurately estimate the individual diffusivity and proliferation parameters of
the model. From this knowledge, the spatio-temporal evolution of the tumour
cell density distribution at later time points can ultimately be precisely
captured using the model. We finally show the applicability of our approach to
MR data of a real glioblastoma patient. This approach may open the perspective
of a clinical application of reaction-diffusion growth models for tumour
prognosis and treatment planning.
