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Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

Research output: Contribution to journal › Article › peer-review

Standard Standard

Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy. / Vidal, F.P.; Villard, P.F.; Lutton, E.
In: IEEE Transactions on Biomedical Engineering, Vol. 59, No. 10, 01.10.2012, p. 2942-2949.

Research output: Contribution to journal › Article › peer-review

HarvardHarvard

Vidal, FP, Villard, PF & Lutton, E 2012, 'Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy', IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp. 2942-2949. https://doi.org/10.1109/TBME.2012.2213251

APA

Vidal, F. P., Villard, P. F., & Lutton, E. (2012). Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy. IEEE Transactions on Biomedical Engineering, 59(10), 2942-2949. https://doi.org/10.1109/TBME.2012.2213251

CBE

Vidal FP, Villard PF, Lutton E. 2012. Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy. IEEE Transactions on Biomedical Engineering. 59(10):2942-2949. https://doi.org/10.1109/TBME.2012.2213251

MLA

Vidal, F.P., P.F. Villard and E. Lutton. "Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy". IEEE Transactions on Biomedical Engineering. 2012, 59(10). 2942-2949. https://doi.org/10.1109/TBME.2012.2213251

VancouverVancouver

Vidal FP, Villard PF, Lutton E. Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy. IEEE Transactions on Biomedical Engineering. 2012 Oct 1;59(10):2942-2949. doi: 10.1109/TBME.2012.2213251

Author

Vidal, F.P. ; Villard, P.F. ; Lutton, E. / Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy. In: IEEE Transactions on Biomedical Engineering. 2012 ; Vol. 59, No. 10. pp. 2942-2949.

RIS

TY - JOUR

T1 - Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

AU - Vidal, F.P.

AU - Villard, P.F.

AU - Lutton, E.

PY - 2012/10/1

Y1 - 2012/10/1

N2 - 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 patient's 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: 1) three patient datasets have been acquired with the “breath hold” protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.

AB - 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 patient's 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: 1) three patient datasets have been acquired with the “breath hold” protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.

U2 - 10.1109/TBME.2012.2213251

DO - 10.1109/TBME.2012.2213251

M3 - Article

VL - 59

SP - 2942

EP - 2949

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 10

ER -