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A generative approach for image-based modeling of tumor growth

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dc.contributor.author Menze, Bjoern Holger
dc.contributor.author Leemput, Koen Van
dc.contributor.author Honkela, Antti
dc.contributor.author Konukoglu, Ender
dc.contributor.author Weber, Marc-Andre
dc.contributor.author Ayache, Nicholas
dc.contributor.author Golland, Polina
dc.date.accessioned 2012-10-10T20:26:44Z
dc.date.available 2012-10-10T20:26:44Z
dc.date.issued 2011-06
dc.date.submitted 2011-07
dc.identifier.isbn 978-3-642-22091-3
dc.identifier.uri http://hdl.handle.net/1721.1/73872
dc.description 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings en_US
dc.description.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. en_US
dc.description.sponsorship German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10) en_US
dc.description.sponsorship Academy of Finland (133611) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (NCRR NAC P41- RR13218) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (NINDS R01-NS051826) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (NIH R01-NS052585) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (NIH R01-EB006758) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (NIH R01-EB009051) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (NIH P41-RR014075) en_US
dc.description.sponsorship National Science Foundation (U.S.) (CAREER Award 0642971) en_US
dc.language.iso en_US
dc.publisher Springer Berlin / Heidelberg en_US
dc.relation.isversionof http://dx.doi.org/10.1007/978-3-642-22092-0_60 en_US
dc.rights Creative Commons Attribution-Noncommercial-Share Alike 3.0 en_US
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/ en_US
dc.source Other University Web Domain en_US
dc.title A generative approach for image-based modeling of tumor growth en_US
dc.type Article en_US
dc.identifier.citation Menze, Bjoern H. et al. “A Generative Approach for Image-Based Modeling of Tumor Growth.” Information Processing in Medical Imaging. Ed. Gábor Székely & Horst K. Hahn. LNCS Vol. 6801. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. 735–747. en_US
dc.contributor.department Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science en_US
dc.contributor.mitauthor Menze, Bjoern Holger
dc.contributor.mitauthor Leemput, Koen Van
dc.contributor.mitauthor Golland, Polina
dc.relation.journal Information Processing in Medical Imaging en_US
dc.identifier.mitlicense OPEN_ACCESS_POLICY en_US
dc.eprint.version Author's final manuscript en_US
dc.type.uri http://purl.org/eprint/type/ConferencePaper en_US
dspace.orderedauthors Menze, Bjoern H.; Leemput, Koen; Honkela, Antti; Konukoglu, Ender; Weber, Marc-André; Ayache, Nicholas; Golland, Polina en


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