A Learnable Prior Improves Inverse Tumor Growth Modeling

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%.

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Publication Details

Journal
IEEE Transactions on Medical Imaging
Published
2024-11-08
DOI
https://doi.org/10.1109/tmi.2024.3494022
Citations
5
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
1.98

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article

A Learnable Prior Improves Inverse Tumor Growth Modeling

Bjoern Menze, Daniel Rueckert, Marie‐Christin Metz, Jana Lipková et al.
5 citations
IEEE Transactions on Medical Imaging
Mathematical Biology Tumor Growth
1.98
article

A Learnable Prior Improves Inverse Tumor Growth Modeling

Bjoern Menze, Daniel Rueckert, Marie‐Christin Metz, Jana Lipková, Jonas Latz, Sebastian Kaltenbach, Ivan Ezhov, Benedikt Wiestler, Michał Balcerak, Leonhard Feiner, Laurin Lux, Sergey Litvinov, Florian Kofler, Jonas Weidner
article en
5 citations

Abstract

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%.

IEEE Transactions on Medical ImagingVol. 44(3)
Harvard University (US), University of Zurich (CH), University of California, Irvine (US), University of Manchester (GB), Helmholtz Zentrum München (DE), Institute of Group Analysis (GB), Imperial College London (GB), Technical University of Munich (DE)
Deutsche Forschungsgemeinschaft, National Institutes of Health, Horizon 2020 Framework Programme
Good health and well-being
Openalex Percentile: Top 16%
Mathematical Biology Tumor Growth
1.98
DOIOpen Access PDFOpenAlex
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