Tuning of patient specific deformable models using an adaptive evolutionary optimization strategy

Franck Vidal 1, * Pierre-Frédéric Villard 2, * Evelyne Lutton 3
* Corresponding author
2 MAGRIT - Visual Augmentation of Complex Environments
Inria Nancy - Grand Est, LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
3 AVIZ - Analysis and Visualization
Inria Saclay - Ile de France
Abstract : We present and analyze the behavior of an evolutionary algorithm designed to estimate the parameters of a complex organ behavior model. The model is adaptable to account for patients specificities. The aim is to finely tune the model to be accurately adapted to various real patient datasets. It can then be embedded, for example, in high fidelity simulations of the human physiology. We present here an application focused on respiration modeling. The algorithm is automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimized. The algorithm efficiency is experimentally analyzed on several real test-cases: i) three patient datasets have been acquired with the breath hold protocol, and ii) two datasets corresponds to 4D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent), a random search and a basic realvalued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.
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Franck Vidal, Pierre-Frédéric Villard, Evelyne Lutton. Tuning of patient specific deformable models using an adaptive evolutionary optimization strategy. IEEE Transactions on Biomedical Engineering, Institute of Electrical and Electronics Engineers, 2012, 59 (10), pp.2942 - 2949. ⟨10.1109/TBME.2012.2213251⟩. ⟨hal-00731910⟩

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