no code implementations • 5 Mar 2024 • Valentina Scarponi, Michel Duprez, Florent Nageotte, Stéphane Cotin
Deep Reinforcement Learning approaches have shown promise in learning this task and may be the key to automating catheter navigation during robotized interventions.
no code implementations • 22 Dec 2023 • Pablo Alvarez, Stéphane Cotin
Numerous regularization methods for deformable image registration aim at enforcing smooth transformations, but are difficult to tune-in a priori and lack a clear physical basis.
no code implementations • 16 Mar 2023 • Alban Odot, Guillaume Mestdagh, Yannick Privat, Stéphane Cotin
Surface matching usually provides significant deformations that can lead to structural failure due to the lack of physical policy.
no code implementations • 15 Dec 2022 • François Lecomte, Jean-Louis Dillenseger, Stéphane Cotin
From this dataset, a neural network is trained to recover the unknown 3D displacement field from a single projection image.
no code implementations • 17 Sep 2021 • Alban Odot, Ryadh Haferssas, Stéphane Cotin
In this paper, we propose a solution to simulate hyper-elastic materials using a data-driven approach, where a neural network is trained to learn the non-linear relationship between boundary conditions and the resulting displacement field.
no code implementations • 13 Dec 2019 • Jaime Garcia Guevara, Igor Peterlik, Marie-Odile Berger, Stéphane Cotin
ACGM is better than the previous Biomechanical Graph Matching method 3 (BGM) because it uses an efficient biomechanical vascularized liver model to compute the organ's transformation and the vessels bifurcations compliance.
no code implementations • 10 Apr 2019 • Andrea Mendizabal, Pablo Márquez-Neila, Stéphane Cotin
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.