Image-driven Stochastic Identification of Boundary Conditions
for Predictive Simulation

D 2017

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

PETERLÍK, Igor, Nazim HAOUCHINE, Lukáš RUČKA and Stéphane COTIN

Basic information

Original name

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Authors

PETERLÍK, Igor (703 Slovakia, guarantor), Nazim HAOUCHINE (12 Algeria), Lukáš RUČKA (203 Czech Republic, belonging to the institution) and Stéphane COTIN (250 France)

Edition

Cham, Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II, p. 548-556, 9 pp. 2017

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/17:00097947

Organization unit

Faculty of Informatics

ISBN

978-3-319-66184-1

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-66185-8_62

Keywords in English

Boundary conditions Stochasric data assimilation Finite element method Surgical augmented reality Hepatic surgery

Abstract

V originále

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.

Links

MUNI/A/0897/2016, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VI.

Investor: Masaryk University, Category A

Citovat

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.

@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.

Detailed Information on Publication Record