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A learnable prior improves inverse tumor growth modeling.
IEEE Trans. Med. Imaging, DOI: 10.1109/TMI.2024.3494022 (2024)
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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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Cma-es ; Evolutionary Sampling ; Individualized Brain Tumor Modeling ; Inverse Biophysics ; Learnable Prior ; Mri
ISSN (print) / ISBN
0278-0062
e-ISSN
1558-254X
Zeitschrift
IEEE Transactions on Medical Imaging
Verlag
Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort
New York, NY [u.a.]
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed