Estimation of boundary conditions for patient-specific liver simulation during augmented surgery



Augmented reality can improve the outcome of hepatic surgeries, assuming an accurate liver model is available to estimate the position of internal structures. While researchers have proposed patient-specific liver simulations, very few have addressed the question of boundary conditions. Resulting mainly from ligaments attached to the liver, they are not visible in preoperative images, yet play a key role in the computation of the deformation.


We propose to estimate both the location and stiffness of ligaments by using a combination of a statistical atlas, numerical simulation, and Bayesian inference. Ligaments are modeled as polynomial springs connected to a liver finite element model. They are initialized using an anatomical atlas and stiffness properties taken from the literature. These characteristics are then corrected using a reduced-order unscented Kalman filter based on observations taken from the laparoscopic image stream.


Our approach is evaluated using synthetic data and phantom data. By relying on a simplified representation of the ligaments to speed up computation times, it is not estimating the true characteristics of ligaments. However, results show that our estimation of the boundary conditions still improves the accuracy of the simulation by 75% when compared to typical methods involving Dirichlet boundary conditions.


By estimating patient-specific boundary conditions, using tracked liver motion from RGB-D data, our approach significantly improves the accuracy of the liver model. The method inherently handles noisy observations, a substantial feature in the context of augmented reality.

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This study was funded from the EU H2020 Research and Innovation programme under Marie Sklodowska-Curie Grant Agreement No. 722068.

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Correspondence to Sergei Nikolaev.

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Nikolaev, S., Cotin, S. Estimation of boundary conditions for patient-specific liver simulation during augmented surgery. Int J CARS 15, 1107–1115 (2020).

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  • Patient-specific modeling
  • Numerical simulation
  • Data assimilation
  • Augmented reality
  • Biomechanics
  • Computer-aided surgery