@inproceedings{6d244c0c88c1413ab32d573284b4a139,
title = "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.",
author = "Menze, {Bjoern H.} and {Van Leemput}, Koen and Antti Honkela and Ender Konukoglu and Weber, {Marc Andr{\'e}} and Nicholas Ayache and Polina Golland",
year = "2011",
doi = "10.1007/978-3-642-22092-0_60",
language = "English",
isbn = "9783642220913",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "735--747",
booktitle = "Information Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings",
note = "22nd International Conference on Information Processing in Medical Imaging, IPMI 2011 ; Conference date: 03-07-2011 Through 08-07-2011",
}