Publications of Andrea Mendizabal
2019
Preprints, Working Papers, …
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- titre
- Simulation of hyperelastic materials in real-time using deep learning
- auteur
- Andrea Mendizabal, Pablo Márquez-Neila, Stéphane Cotin
- article
- 2019
- resume
- The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm. We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. Our method is applied to two benchmark examples: a cantilever beam and an L-shape subject to moving punctual loads. A comparison between our method and proper orthogonal decomposition (POD) is done through the paper. The results show that U-Mesh can perform very fast simulations on various geometries, mesh resolutions and number of input forces with very small errors.
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2018
Conference papers
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- titre
- A Combined Simulation & Machine Learning Approach for Image-based Force Classification during Robotized Intravitreal Injections
- auteur
- Andrea Mendizabal, Tatiana Fountoukidou, Jan Hermann, Raphael Sznitman, Stéphane Cotin
- article
- MICCAI 2018 – 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2018, Granada, Spain
- resume
- Intravitreal injection is one of the most common treatment strategies for chronic ophthalmic diseases. The last decade has seen the number of intravitreal injections dramatically increase, and with it, adverse effects and limitations. To overcome these issues, medical assistive devices for robotized injections have been proposed and are projected to improve delivery mechanisms for new generation of pharmacological solutions. In our work, we propose a method aimed at improving the safety features of such envisioned robotic systems. Our vision-based method uses a combination of 2D OCT data, numerical simulation and machine learning to estimate the range of the force applied by an injection needle on the sclera. We build a Neural Network (NN) to predict force ranges from Optical Coherence Tomography (OCT) images of the sclera directly. To avoid the need of large training data sets, the NN is trained on images of simulated deformed sclera. We validate our approach on real OCT images collected on five ex vivo porcine eyes using a robotically-controlled needle. Results show that the applied force range can be predicted with 94% accuracy. Being real-time, this solution can be integrated in the control loop of the system, allowing for in-time withdrawal of the needle.
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2017
Conference papers
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- titre
- Face-based Smoothed Finite Element Method for Real-time Simulation of soft tissue
- auteur
- Andrea Mendizabal, Rémi Duparc, Huu Phuoc Bui, Christoph Paulus, Igor Peterlik, Stéphane Cotin
- article
- SPIE Medical Imaging, Feb 2017, Orlando, United States. SPIE Medical Imaging
- resume
- In soft tissue surgery, a tumor and other anatomical structures are usually located using the preoperative CT or MR images. However, due to the deformation of the concerned tissues, this information suffers from inaccuracy when employed directly during the surgery. In order to account for these deformations in the planning process, the use of a bio-mechanical model of the tissues is needed. Such models are often designed using the finite element method (FEM), which is, however, computationally expensive, in particular when a high accuracy of the simulation is required. In our work, we propose to use a smoothed finite element method (S-FEM) in the context of modeling of the soft tissue deformation. This numerical technique has been introduced recently to overcome the overly stiff behavior of the standard FEM and to improve the solution accuracy and the convergence rate in solid mechanics problems. In this paper, a face-based smoothed finite element method (FS-FEM) using 4-node tetrahedral elements is presented. We show that in some cases, the method allows for reducing the number of degrees of freedom, while preserving the accuracy of the discretization. The method is evaluated on a simulation of a cantilever beam loaded at the free end and on a simulation of a 3D cube under traction and compression forces. Further, it is applied to the simulation of the brain shift and of the kidney’s deformation. The results demonstrate that the method outperforms the standard FEM in a bending scenario and that has similar accuracy as the standard FEM in the simulations of brain shift and kidney deformation.
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