Abstract
Brain tumor segmentation and brain tumor growth assessment are inter-dependent and benefit 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 infinite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the different image modalities. In this study, we validate this model to obtain brain tumor segmentations and volumetry in longitudinal image data. Moreover, we use the model to develop a probabilistic framework for estimating the likelihood of disease progression, i.e. tumor regrowth, after therapy. We present experiments for longitudinal image sequences with 
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and flair images, acquired for ten patients with low and high grade gliomas.
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Alberts, E. et al. (2016). A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR Sequences. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_7
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DOI: https://doi.org/10.1007/978-3-319-30858-6_7
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