We propose a method for solving the strong form of the dynamic Cosserat rod clamped-free boundary value problem (BVP) under frictional contact. The shooting method is employed for BVP solving, and contact response is computed using the penalty method. The proposed method is tailored for accurate and rapid simulation of catheterization-like medical procedures. Our contact scenarios involve complex shapes and real patient geometry, modeled with implicit surfaces as blobby models. The results show that using blobby models for contact detection is orders of magnitude faster than using triangle meshes. This approach provides smoother contact forces, accelerating the convergence of the shooting method. We have made our code available as open source at https://gitlab.inria.fr/rjilani/IROS2024/.

We propose an orthogonal collocation method (CM) for solving Cosserat rod Dirichlet-Neumann boundary value problems in static and dynamic modes. We interpolate the internal loading and collocate the strong form of the differential equations. The method uses Chebyshev polynomials in order to minimize Runge's phenomenon. The time derivatives are implicitly discretized using the backward differentiation formula BDF-$\alpha$We compare our method with the shooting method (SM), multiple shooting method (MSM) and two isogeometric CM against three static and one dynamic applications. The results show that our CM is more stable than SM and faster than MSM.

This paper presents a method for real-time augmented reality of internal liver structures during minimally invasive hepatic surgery. Vessels and tumors computed from pre-operative CT scans can be overlaid onto the laparoscopic view for surgery guidance. Compared to current methods, our method is able to locate the in-depth positions of the tumors based on partial three-dimensional liver tissue motion using a real-time biomechanical model. This model permits to properly handle the motion of internal structures even in the case of anisotropic or heterogeneous tissues, as it is the case for the liver and many anatomical structures. Experimentations conducted on phantom liver permits to measure the accuracy of the augmentation while real-time augmentation on in vivo human liver during real surgery shows the benefits of such an approach for minimally invasive surgery.

This article introduces a method for tracking the internal structures of the liver during robot-assisted procedures. Vascular network, tumors and cut planes, computed from pre-operative data, can be overlaid onto the laparoscopic view for image-guidance, even in the case of large motion or deformation of the organ. Compared to current methods, our method is able to precisely propagate surface motion to the internal structures. This is made possible by relying on a fast yet accurate biomechanical model of the liver combined with a robust visual tracking approach designed to properly constrain the model. Augmentation results are demonstrated on in-vivo sequences of a human liver during robotic surgery, while quantitative validation is performed on an ex-vivo porcine liver experimentation. Validation results show that our approach gives an accurate surface registration with an error of less than 6mm on the position of the tumor.

This paper presents a method for real-time augmentation of vascular network and tumors during minimally invasive liver surgery. Internal structures computed from pre-operative CT scans can be overlaid onto the laparoscopic view for surgery guidance. Compared to state-of-the-art methods, our method uses a real-time biomechanical model to compute a volumetric displacement field from partial three-dimensional liver surface motion. This permits to properly handle the motion of internal structures even in the case of anisotropic or heterogeneous tissues, as it is the case for the liver and many anatomical structures. Real-time augmentation results are presented on in vivo and phantom data and illustrate the benefits of such an approach for minimally invasive surgery.

We propose a shape-based variational framework to curve evolution for the segmentation of tongue contours from MRI mid-sagittal images. In particular, we first build a PCA model on tongue contours of different articulations of a reference speaker, and use it as shape priors. The parameters of the curve representation are then manipulated to minimize an objective function. The designed energy integrates both global and local image information. The global term extracts roughly the object in the whole image domain; while the local term improves precision inside a small neighborhood around the contour. Promising results and comparisons with other approaches demonstrate the efficiency of our new model.

Being able to animate a speech production model with articulatory data would open applications in many domains. In this paper, we first consider the problem of acquiring articulatory data from non invasive image and sensor modalities: dynamic ultrasound (US) images, stereovision 3D data, electromagnetic sensors and MRI. We here especially focus on automatic registration methods which enable the fusion of the articulatory features in a common frame. We then derive articulatory parameters by fitting these features with Maeda's model. To our knowledge, it is the first attempt to derive articulatory parameters from features automatically extracted and registered between the modalities. Results prove the soundness of the approach and the reliability of the fused articulatory data.

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