Abstract: Deep probabilistic modeling of glioma growth

Abstract

Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters. In this work, we propose to learn growth dynamics directly from annotated MR image data, without specifying an explicit model, leveraging recent developments in deep generative models. We further assume that imaging is ambiguous with respect to the underlying disease, which is reflcted in our approach in that it doesn’t predict a single growth estimate but instead estimates a distribution of plausible changes for a given tumor.