Stochastic Correction of Boundary Conditions during Liver Surgery

Abstract : Boundary conditions play essential role in forming the predictive capacity of the biomechanical model of liver, which is used to facilitate the intra-operative navigation during surgery operation. However, these conditions are presented mainly by ligaments, and the properties of them cant be measured reliably. Therefore, the idea is to propose the data assimilation approach where the deformation of the liver tissue is used to estimate the organ attachments. The behavior of liver can be recorded on stereo camera, ultrasound, or some other modality, but, due to observational errors, there is a high amount of uncertainty in the system. One possible option is to model boundary conditions as stochastic values and to use the reduced-order unscented Kalman filter for their estimation. This filter obtains the result as iterative process based on computation of the weighted average between observed data and the physically-based model that simulates behavior of liver. Based on an approach previously proposed in [1], this work is focused on estimation of the modified boundary conditions, which happens when ligaments are cut during surgery process. This estimation incorporates changes in liver shape movement under various manipulations. The method is evaluated using synthetic data in two scenarios: the deformation of parallelepiped mesh under single direction straight forward deformation and the deformation of liver model under more complex movements.
Type de document :
Communication dans un congrès
CVCS 2018 - 9th Colour and Visual Computing Symposium 2018, Sep 2018, Gjovik, Norway. 2018
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Contributeur : Sergei Nikolaev <>
Soumis le : jeudi 12 juillet 2018 - 09:47:13
Dernière modification le : mercredi 29 août 2018 - 01:05:53


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  • HAL Id : hal-01823810, version 1



Sergei Nikolaev, Igor Peterlik, Stéphane Cotin. Stochastic Correction of Boundary Conditions during Liver Surgery. CVCS 2018 - 9th Colour and Visual Computing Symposium 2018, Sep 2018, Gjovik, Norway. 2018. 〈hal-01823810〉



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