A Nonparametric model for Brain Tumor Segmentation and Volumetry in Longitudinal MR Sequences

Esther Alberts 1 Guillaume Charpiat 2 Yuliya Tarabalka 3 Thomas Huber 4 Marc-André Weber 5 Jan Bauer 4 Claus Zimmer 4 Bjoern H. Menze 1
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
3 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Brain tumor image segmentation and brain tumor growth assessment are inter-dependent and bene t from a joint evaluation. Starting from a generative model for multimodal brain tumor segmentation, we make use of a nonparametric growth model that is implemented as a conditional random field (CRF) including directed links with in finite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the di erent image modalities. In this study, we validate this model to obtain brain tumor segmentations and volumetry in longitudinal image data. Moreover, we extend the framework with a probabilistic approach for estimating the likelihood of disease progression, i.e. tumor regrowth, after therapy. We present experiments for longitudinal image sequences with T1, T1c, T2 and FLAIR images, acquired for ten patients with low and high grade gliomas.
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Esther Alberts, Guillaume Charpiat, Yuliya Tarabalka, Thomas Huber, Marc-André Weber, et al.. A Nonparametric model for Brain Tumor Segmentation and Volumetry in Longitudinal MR Sequences. MICCAI Brain Lesion Workshop, Oct 2015, Munich, Germany. ⟨hal-01205916⟩

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