A Generative Approach for Image-Based Modeling of Tumor Growth

Abstract : Extensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.
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Communication dans un congrès
Szekely, G. and Hahn, H. IPMI - 22nd International Conference on Information Processing in Medical Images- 2011, Jul 2011, Kloster Irsee, Germany. Springer, 6801, pp.735-747, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-22092-0_60〉
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https://hal.inria.fr/inria-00616211
Contributeur : Project-Team Asclepios <>
Soumis le : vendredi 19 août 2011 - 19:57:17
Dernière modification le : jeudi 11 janvier 2018 - 16:25:47

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Bjoern H. Menze, Koen Van Leemput, Antti Honkela, Ender Konukoglu, Marc-André Weber, et al.. A Generative Approach for Image-Based Modeling of Tumor Growth. Szekely, G. and Hahn, H. IPMI - 22nd International Conference on Information Processing in Medical Images- 2011, Jul 2011, Kloster Irsee, Germany. Springer, 6801, pp.735-747, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-22092-0_60〉. 〈inria-00616211〉

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