Deep Probabilistic Modeling of Glioma Growth

Jens Petersen; Paul F. Jäger; Fabian Isensee; Simon A. A. Kohl; Ulf Neuberger; Wolfgang Wick; Jürgen Debus; Sabine Heiland; Martin Bendszus; Philipp Kickingereder; Klaus H. Maier-Hein

arXiv · 1907.04064

Deep Probabilistic Modeling of Glioma Growth

Jens Petersen·Paul F. Jäger·Fabian Isensee·Simon A. A. Kohl·Ulf Neuberger·Wolfgang Wick·Jürgen Debus·Sabine Heiland·Martin Bendszus·Philipp Kickingereder·Klaus H. Maier-Hein

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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 an alternative approach based on recent advances in probabilistic segmentation and representation learning that implicitly learns growth dynamics directly from data without an underlying explicit model. We present evidence that our approach is able to learn a distribution of plausible future tumor appearances conditioned on past observations of the same tumor.

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Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein. 2019-07-09. Deep Probabilistic Modeling of Glioma Growth. https://arxiv.org/abs/1907.04064

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