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Weidner, J.* ; Ezhov, I.* ; Balcerak, M.* ; Metz, M.C.* ; Litvinov, S.* ; Kaltenbach, S.* ; Feiner, L.F.* ; Lux, L.* ; Kofler, F. ; Lipkova, J.* ; Latz, J.* ; Rueckert, D.* ; Menze, B.* ; Wiestler, B.*

A learnable prior improves inverse tumor growth modeling.

IEEE Trans. Med. Imaging 44, 1297-1307 (2024)
Verlagsversion Postprint DOI
Open Access Green
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; Glioma Growth; Evolution; Adaptation; Invasion
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Quellenangaben Band: 44, Heft: 3, Seiten: 1297-1307 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530001-001
Förderungen Helmut-Horten-Foundation
NIH
European High Performance Computing Joint Undertaking (EuroHPC)
Deutsche Forschungsgemeinschaft (DFG)
DOI 10.1109/TMI.2024.3494022
WOS ID 001447560800001
Scopus ID 85210182657
Erfassungsdatum 2024-12-02
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