A generative approach for image-based modeling of tumor growth
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Golland_A generative approach.pdf
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Author(s)
Menze, Bjoern Holger
Leemput, Koen Van
Honkela, Antti
Konukoglu, Ender
Weber, Marc-Andre
Ayache, Nicholas
Golland, Polina
Date Issued
June 2011
Journal
Information Processing in Medical Imaging
Publisher
Springer Berlin / Heidelberg
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.
Version
Author's final manuscript
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.
Description
22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/978-3-642-22092-0_60
