Image-Driven Stochastic Identification of Boundary Conditions for Predictive Simulation

  • Igor Peterlik
  • Nazim Haouchine
  • Lukáš Ručka
  • Stéphane CotinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


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.


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

Supplementary material

451304_1_En_62_MOESM1_ESM.mp4 (17 mb)
Supplementary material 1 (mp4 17388 KB)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Igor Peterlik
    • 1
  • Nazim Haouchine
    • 1
  • Lukáš Ručka
    • 2
  • Stéphane Cotin
    • 1
    Email author
  1. 1.InriaParisFrance
  2. 2.Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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