InforLorV4, Main, Exploration, bibRecord, 001919

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

Identifieur interne : 001919 ( Main/Exploration ); précédent : 001918; suivant : 001920

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

Auteurs : Franck Vidal [Royaume-Uni] ; Pierre-Frédéric Villard [France] ; Evelyne Lutton [France]

Source :

IEEE Transactions on Biomedical Engineering [ 0018-9294 ] ; 2012-10-01.

RBID : Hal:hal-00731910

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.

Url:

https://hal.inria.fr/hal-00731910

DOI: 10.1109/TBME.2012.2213251

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Tuning of patient specific deformable models using an adaptive evolutionary optimization strategy

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

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