A generative approach for image-based modeling of tumor growth

Bjoern H. Menze*, Koen Van Leemput, Antti Honkela, Ender Konukoglu, Marc André Weber, Nicholas Ayache, Polina Golland

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

36 Citations (Scopus)

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 multimodal 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.

Original languageEnglish
Title of host publicationINFORMATION PROCESSING IN MEDICAL IMAGING
Pages735-747
Number of pages13
ISBN (Electronic)978-3-642-22092-0
DOIs
Publication statusPublished - 2011
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Processing in Medical Imaging - , Germany
Duration: 3 Jul 20118 Jul 2011
Conference number: 22

Publication series

NameLecture Notes in Computer Science
Volume6801
ISSN (Print)0302-9743

Conference

ConferenceInternational Conference on Information Processing in Medical Imaging
Abbreviated titleIPMI
CountryGermany
Period03/07/201108/07/2011

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