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A Learnable Prior Improves Inverse Tumor Growth Modeling by Weidner, Jonas; Ezhov, Ivan; Balcerak, Michal; Metz, Marie-Christin; Litvinov, Sergey; Kaltenbach, Sebastian; Feiner, Leonhard; Lux, Laurin; Kofler, Florian; Lipkova, Jana; Latz, Jonas; Rueckert, Daniel; Menze, Bjoern; Wiestler, Benedikt is a scholarly article available to read on EtoBox.

What is A Learnable Prior Improves Inverse Tumor Growth Modeling about?

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%.

Author

Weidner, Jonas; Ezhov, Ivan; Balcerak, Michal; Metz, Marie-Christin; Litvinov, Sergey; Kaltenbach, Sebastian; Feiner, Leonhard; Lux, Laurin; Kofler, Florian; Lipkova, Jana; Latz, Jonas; Rueckert, Daniel; Menze, Bjoern; Wiestler, Benedikt

Published

2024

Language

EN