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

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

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.
Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging : 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings
EditorsGábor Székely, Horst K. Hahn
Number of pages13
PublisherSpringer-Verlag
Publication date2011
Pages735-747
ISBN (Print)978-3-642-22091-3
ISBN (Electronic)978-3-642-22092-0
DOIs
Publication statusPublished - 2011
MoE publication typeA4 Article in conference proceedings
EventInternational Conference on Information Processing in Medical Imaging (IPMI 2011) - Kloster Irsee, Germany
Duration: 3 Jul 20118 Jul 2011
Conference number: 22nd

Publication series

NameLecture Notes in Computer Science
Volume6801

Fields of Science

  • 113 Computer and information sciences

Cite this

Menze, B. H., Van Leemput, K., Honkela, A., Konukoglu, E., Weber, M-A., Ayache, N., & Golland, P. (2011). A Generative Approach for Image-Based Modeling of Tumor Growth. In G. Székely, & H. K. Hahn (Eds.), Information Processing in Medical Imaging: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings (pp. 735-747). (Lecture Notes in Computer Science; Vol. 6801). Springer-Verlag. https://doi.org/10.1007/978-3-642-22092-0_60
Menze, Bjoern H ; Van Leemput, Koen ; Honkela, Antti ; Konukoglu, Ender ; Weber, Marc-André ; Ayache, Nicholas ; Golland, Polina. / A Generative Approach for Image-Based Modeling of Tumor Growth. Information Processing in Medical Imaging: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings. editor / Gábor Székely ; Horst K. Hahn. Springer-Verlag, 2011. pp. 735-747 (Lecture Notes in Computer Science).
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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.",
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Menze, BH, Van Leemput, K, Honkela, A, Konukoglu, E, Weber, M-A, Ayache, N & Golland, P 2011, A Generative Approach for Image-Based Modeling of Tumor Growth. in G Székely & HK Hahn (eds), Information Processing in Medical Imaging: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings. Lecture Notes in Computer Science, vol. 6801, Springer-Verlag, pp. 735-747, International Conference on Information Processing in Medical Imaging (IPMI 2011), Kloster Irsee, Germany, 03/07/2011. https://doi.org/10.1007/978-3-642-22092-0_60

A Generative Approach for Image-Based Modeling of Tumor Growth. / Menze, Bjoern H; Van Leemput, Koen; Honkela, Antti; Konukoglu, Ender; Weber, Marc-André; Ayache, Nicholas; Golland, Polina.

Information Processing in Medical Imaging: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings. ed. / Gábor Székely; Horst K. Hahn. Springer-Verlag, 2011. p. 735-747 (Lecture Notes in Computer Science; Vol. 6801).

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

TY - GEN

T1 - A Generative Approach for Image-Based Modeling of Tumor Growth

AU - Menze, Bjoern H

AU - Van Leemput, Koen

AU - Honkela, Antti

AU - Konukoglu, Ender

AU - Weber, Marc-André

AU - Ayache, Nicholas

AU - Golland, Polina

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PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - 113 Computer and information sciences

U2 - 10.1007/978-3-642-22092-0_60

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M3 - Conference contribution

SN - 978-3-642-22091-3

T3 - Lecture Notes in Computer Science

SP - 735

EP - 747

BT - Information Processing in Medical Imaging

A2 - Székely, Gábor

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PB - Springer-Verlag

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Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber M-A, Ayache N et al. A Generative Approach for Image-Based Modeling of Tumor Growth. In Székely G, Hahn HK, editors, Information Processing in Medical Imaging: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings. Springer-Verlag. 2011. p. 735-747. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-22092-0_60