| PETERLÍK, Igor, Nazim HAOUCHINE, Lukáš RUČKA and Stéphane COTIN. Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation. Online. In Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II. Cham: Springer, 2017, p. 548-556. ISBN 978-3-319-66184-1. Available from: https://dx.doi.org/10.1007/978-3-319-66185-8_62. |
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@inproceedings{1392838,
author = {Peterlík, Igor and Haouchine, Nazim and Ručka, Lukáš and Cotin, Stéphane},
address = {Cham},
booktitle = {Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II},
doi = {http://dx.doi.org/10.1007/978-3-319-66185-8_62},
editor = {Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S.},
keywords = {Boundary conditions Stochasric data assimilation Finite element method Surgical augmented reality Hepatic surgery},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Cham},
isbn = {978-3-319-66184-1},
pages = {548-556},
publisher = {Springer},
title = {Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation},
url = {https://doi.org/10.1007/978-3-319-66185-8_62},
year = {2017}
}
TY - JOUR
ID - 1392838
AU - Peterlík, Igor - Haouchine, Nazim - Ručka, Lukáš - Cotin, Stéphane
PY - 2017
TI - Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation
PB - Springer
CY - Cham
SN - 9783319661841
KW - Boundary conditions Stochasric data assimilation Finite element method Surgical augmented reality Hepatic surgery
UR - https://doi.org/10.1007/978-3-319-66185-8_62
L2 - https://doi.org/10.1007/978-3-319-66185-8_62
N2 - In computer-aided interventions, biomechanical models reconstructed from the pre-operative data are used via augmented reality to facilitate the intra-operative navigation. The predictive power of such models highly depends on the knowledge of boundary conditions. However, in the context of patient-specific modeling, neither the pre-operative nor the intra-operative modalities provide a reliable information about the location and mechanical properties of the organ attachments. We present a novel image-driven method for fast identification of boundary conditions which are modelled as stochastic parameters. The method employs the reduced-order unscented Kalman filter to transform in real-time the probability distributions of the parameters, given observations extracted from intra-operative images. The method is evaluated using synthetic, phantom and real data acquired in vivo on a porcine liver. A quantitative assessment is presented and it is shown that the method significantly increases the predictive power of the biomechanical model.
ER -
PETERLÍK, Igor, Nazim HAOUCHINE, Lukáš RUČKA and Stéphane COTIN. Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation. Online. In Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. \textit{Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II}. Cham: Springer, 2017, p.~548-556. ISBN~978-3-319-66184-1. Available from: https://dx.doi.org/10.1007/978-3-319-66185-8\_{}62.
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