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 a Stéphane COTIN

Základní údaje

Originální název

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Autoři

PETERLÍK, Igor (703 Slovensko, garant); Nazim HAOUCHINE (12 Alžírsko); Lukáš RUČKA (203 Česká republika, domácí) a Stéphane COTIN (250 Francie)

Vydání

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

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/17:00097947

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-66184-1

ISSN

DOI

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

EID Scopus

2-s2.0-85029470491

Klíčová slova anglicky

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

Štítky

core_A, firank_A

Změněno: 17. 5. 2018 16:44, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

MUNI/A/0897/2016, interní kód MU

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

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VI., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

PETERLÍK, Igor; Nazim HAOUCHINE; Lukáš RUČKA a 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, s. 548-556. ISBN 978-3-319-66184-1. Dostupné z: https://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 = {https://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  - CONF
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 a 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, s.~548-556. ISBN~978-3-319-66184-1. Dostupné z: https://doi.org/10.1007/978-3-319-66185-8\_{}62.

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