A generative approach for image-based modeling of tumor growth
22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Springer Berlin / Heidelberg
2012
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| Online Access: | http://hdl.handle.net/1721.1/73872 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1826202881131282432 |
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| author | Menze, Bjoern Holger Leemput, Koen Van Honkela, Antti Konukoglu, Ender Weber, Marc-Andre Ayache, Nicholas Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Menze, Bjoern Holger Leemput, Koen Van Honkela, Antti Konukoglu, Ender Weber, Marc-Andre Ayache, Nicholas Golland, Polina |
| author_sort | Menze, Bjoern Holger |
| collection | MIT |
| description | 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings |
| first_indexed | 2024-09-23T12:24:09Z |
| format | Article |
| id | mit-1721.1/73872 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:24:09Z |
| publishDate | 2012 |
| publisher | Springer Berlin / Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/738722022-09-28T07:54:37Z A generative approach for image-based modeling of tumor growth Menze, Bjoern Holger Leemput, Koen Van Honkela, Antti Konukoglu, Ender Weber, Marc-Andre Ayache, Nicholas Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Menze, Bjoern Holger Leemput, Koen Van Golland, Polina 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings 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. German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10) Academy of Finland (133611) National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149) National Institutes of Health (U.S.) (NCRR NAC P41- RR13218) National Institutes of Health (U.S.) (NINDS R01-NS051826) National Institutes of Health (U.S.) (NIH R01-NS052585) National Institutes of Health (U.S.) (NIH R01-EB006758) National Institutes of Health (U.S.) (NIH R01-EB009051) National Institutes of Health (U.S.) (NIH P41-RR014075) National Science Foundation (U.S.) (CAREER Award 0642971) 2012-10-10T20:26:44Z 2012-10-10T20:26:44Z 2011-06 2011-07 Article http://purl.org/eprint/type/ConferencePaper 978-3-642-22091-3 http://hdl.handle.net/1721.1/73872 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. https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1007/978-3-642-22092-0_60 Information Processing in Medical Imaging Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Springer Berlin / Heidelberg Other University Web Domain |
| spellingShingle | Menze, Bjoern Holger Leemput, Koen Van Honkela, Antti Konukoglu, Ender Weber, Marc-Andre Ayache, Nicholas Golland, Polina A generative approach for image-based modeling of tumor growth |
| title | A generative approach for image-based modeling of tumor growth |
| title_full | A generative approach for image-based modeling of tumor growth |
| title_fullStr | A generative approach for image-based modeling of tumor growth |
| title_full_unstemmed | A generative approach for image-based modeling of tumor growth |
| title_short | A generative approach for image-based modeling of tumor growth |
| title_sort | generative approach for image based modeling of tumor growth |
| url | http://hdl.handle.net/1721.1/73872 https://orcid.org/0000-0003-2516-731X |
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