Simulation of hyperelastic materials in real-time using Deep Learning

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

Real-time Error Control for Surgical Simulation

Objective: To present the first real-time a posteriori error-driven adaptive finite element approach for real-time simulation and to demonstrate the method on a needle insertion problem. Methods: We use corotational elasticity and a frictional needle/tissue interaction model. The problem is solved using finite elements within SOFA. The refinement strategy relies upon a hexahedron-based finite element method, combined with a posteriori error estimation driven local $h$-refinement, for simulating soft tissue deformation. Results: We control the local and global error level in the mechanical fields (e.g. displacement or stresses) during the simulation. We show the convergence of the algorithm on academic examples, and demonstrate its practical usability on a percutaneous procedure involving needle insertion in a liver. For the latter case, we compare the force displacement curves obtained from the proposed adaptive algorithm with that obtained from a uniform refinement approach. Conclusions: Error control guarantees that a tolerable error level is not exceeded during the simulations. Local mesh refinement accelerates simulations. Significance: Our work provides a first step to discriminate between discretization error and modeling error by providing a robust quantification of discretization error during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM

Calipso: Physics-based Image and Video Editing through CAD Model Proxies

We present Calipso, an interactive method for editing images and videos in a physically-coherent manner. Our main idea is to realize physics-based manipulations by running a full physics simulation on proxy geometries given by non-rigidly aligned CAD models. Running these simulations allows us to apply new, unseen forces to move or deform selected objects, change physical parameters such as mass or elasticity, or even add entire new objects that interact with the rest of the underlying scene. In Calipso, the user makes edits directly in 3D; these edits are processed by the simulation and then transfered to the target 2D content using shape-to-image correspondences in a photo-realistic rendering process. To align the CAD models, we introduce an efficient CAD-to-image alignment procedure that jointly minimizes for rigid and non-rigid alignment while preserving the high-level structure of the input shape. Moreover, the user can choose to exploit image flow to estimate scene motion, producing coherent physical behavior with ambient dynamics. We demonstrate Calipso's physics-based editing on a wide range of examples producing myriad physical behavior while preserving geometric and visual consistency.Comment: 11 page

Population size, distribution and habitat use of the Hawaiian Short-eared Owl (Asio flammeus sandwichensis) on O'ahu

Technical report prepared for the State of Hawaiʻi Department of Land & Natural Resources on population size, distribution and habitat use of the Hawaiian Short-eared Owl (Asio flammeus sandwichensis) on O'ahuThe Pueo (Asio flammeus sandwichensis), once common across the Hawaiian Islands, is currently state-listed as Endangered on O'ahu. The Pueo provides important ecosystem services by controlling population sizes of introduced rodents and preying on other introduced and native species, including birds and invertebrates. As the only native raptor that breeds on the main Hawaiian Islands, the Pueo plays an important role in top-down ecological regulation and is also valued by native Hawaiians and other Hawai'i residents. Although the Pueo has been recorded in a variety of vegetation types in the Hawaiian archipelago, key habitat selection variables are still unknown. In this study, we optimized a survey methodology to improve population estimates and define vegetation types important to population stability and we compared distribution among vegetation types and overall population densities of Pueo with other Short-eared Owl populations across the globe. Three different approaches were used: (a) standardized surveys by trained personnel; (b) citizen science reports of Pueo sightings submitted to an online portal www.pueoproject.com; and (c) citizen science reports to eBird www.ebird.org, a publicly available, well-established, and curated international online portal for submitting bird sighting reports. We collected more than 50 Pueo sightings in one year through the Pueo project online portal, while the eBird portal collected 43 reports in three decades. Information gathered through the citizen science portal was highly valuable for obtaining phenology and breeding event observations (nests, owlet locations, display flights), however, data collected in this manner were biased due to the lack of standard distribution of the observers, which hampered their usefulness for running distribution models or other population analyses. During the standardized surveys Pueo were observed on agricultural lands, wetlands, short grasslands and open native vegetation. Pueo were detected, on average, 23 minutes before twilight. Estimated densities ranged from 0 to 3.3 Pueo per 100 ha across vegetation types, with most detections occurring in open vegetation types, such as agricultural lands, grasslands, and wetlands. Based on observed densities, the population of Pueo inhabiting O'ahu was estimated at 807 individuals, with 95% confidence intervals of 8 to 2199. Densities obtained from standardized, randomized surveys are aligned with those studies targeting known Short-eared Owl populations with a high rate of occupancy, which does not seem to be the situation on O'ahu, especially if we consider the high level of threats that this species faces in Hawai'i and the observations of declining populations that local inhabitants have reported in person or submitted to the Pueo Project portal. Densities on O'ahu are probably similar to the ones reported in non-targeted, randomized and standardized studies, where owls occupy territories with high prey availability, but leave unoccupied low-prey-density territories. Based on this information, we consider the most likely population number to be on the lower end of the estimated range of possibilities

Robust RANSAC-based blood vessel segmentation

International audienceMany vascular clinical applications require a vessel segmentation process that is able to both extract the centerline and the surface of the blood vessels. However, noise and topology issues (such as kissing vessels) prevent existing algorithms from being able to easily retrieve such a complex system as the brain vasculature. We propose here a new blood vessel tracking algorithm that 1) detect the vessel centerline; 2) provide a local radius estimate; and 3) extracts a dense set of points at the blood vessel surface. This algorithm is based on a RANSAC-based robust fitting of successive cylinders along the vessel. Our method was validated against the Multiple Hypothesis Testing (MHT) algorithm on 10 3DRA patient data of the brain vasculature. Over 30 blood vessels of various sizes were considered for each patient. Our results demonstrated a greater ability of our algorithm to track small, tortuous and touching vessels (96% success rate), compared to MHT (65% success rate). The computed centerline precision was below 1 voxel when compared to MHT. Moreover, our results were obtained with the same set of parameters for all patients and all blood vessels, except for the seed point for each vessel, also necessary for MHT. The proposed algorithm is thereafter able to extract the full intracranial vasculature with little user interaction

Augmented Reality for Cryoablation Procedures

International audienceCryotherapy is a rapidly growing minimally invasive technique for the treatment of different kinds of tumors, such as breast cancer, renal and prostate cancer. Several hollow needles are percutaneously inserted in the target area under image guidance and a gas (usually argon) is then decompressed inside the needles. Based on the Thompson-Joule principle, the temperature drops drown and a ball of ice crystals forms around the tip of each needle. Radiologists rely on the geometry of this iceball (273K), visible on computer tomographic (CT) or magnetic resonance (MR) images, to assess the status of the ablation. However, cellular death only occurs when the temperature falls below 233K. The complexity of the procedure therefore resides in planning the optimal number, position and orientation of the needles required to treat the tumor, while avoiding any damage to the surrounding healthy tissues.This planning is currently done qualitatively, based on experience, and can take several hours, with a result that is often different from the expected one. To solve this important limitation of cryotherapy, a few planning systems have been proposed in the literature. Currently, commercial systems are nearly non existent, and emerging tools are limited to a visualization of the isotherms obtained for each needle in ideal conditions (usually in a gel). They do not account for any influence of the soft tissue properties, the presence of blood vessels, or the combined effect of multiple needles. As a consequence, large safety margins over 5mm are defined.To address this challenge, our method extracts information from medical images (CT or MR) and allows to assess different strategies with an augmented visualization of the resulting iceball and the associated isotherms

A Shell Model for Real-time Simulation of intra-ocular Implant Deployment

International audienceWith 30 million interventions a year worldwide, cataract surgery is one of the most frequently performed procedures. Yet, no tool currently allows teaching all steps of the procedure without putting pa- tients at risk. A particularly challenging stage of this surgery deals with the injection and deployment of the intra-ocular lens implant. In this paper we propose to rely on shell theory to accurately describe the com- plex deformations of the implant. Our approach extends the co-rotational method used in finite element analysis of in-plane deformations to incor- porate a bending energy. This results in a relatively simple and compu- tationally efficient approach which was applied to the simulation of the lens deployment. This simulation also accounts for the complex contacts that take place during the injection phase

Computation and Visualization of Risk Assessment in Deep Brain Stimulation

International audienceDeep Brain Stimulation is a neurosurgical approach for the treatment of pathologies such as Parkinson's disease. The basic principle consists in placing a thin electrode in a deep part of the brain. To safely reach the target of interest, careful planning must be performed to ensure that no vital structure (e.g. blood vessel) will be damaged during the insertion of the electrode. Currently this planning phase is done without considering the brain shift, which occurs during the surgery once the skull is open, leading to increased risks of complications. In this paper, we propose a method to compute the motion of anatomical structures induced by the brain shift. This computation is based on a biomechanical model of the brain and the cerebro-spinal fluid. We then visualize in a intuitive way the risk of damaging vital structures with the electrode.La stimulation cérébrale profonde est une procédure neurochirurgicale pour le traitement de pathologies comme la maladie de Parkinson. La procédure consiste à implanter une électrode dans une région profonde du cerveau. Pour atteindre la cible sans risque, le chirurgien procède à une plannification minutieuse pour s'assurer qu'aucune structure vitale (vaisseaux sanguins, ventricules) ne se retrouve sur le chemin de l'électrode. Actuellement, la plannification ne considère pas les déformations intra-opératoires, qui se produisent une fois que le crâne est ouvert. Cela peut entraîner des compolications. Dans ce papier, nous proposons une méthode pour calculer le risque de mouvement des structures anatomiques causés par ces déformations. Le calcul s'appuie sur un modèle biomécanique du cerveau et du fluide céphalo-rachidien. Nous visualisons ensuite intuitivement le risque d'endommager une structure vitale avec l'électrode

Asynchronous haptic simulation of contacting deformable objects with variable stiffness

International audienceAbstract--This paper presents a new asynchronous approach for haptic rendering of deformable objects. When stiff nonlinear deformations take place, they introduce important and rapid variations of the force sent to the user. This problem is similar to the stiff virtual wall for which a high refresh rate is required to obtain a stable haptic feedback. However, when dealing with several interacting deformable objects, it is usually impossible to simulate all objects at high rates. To address this problem we propose a quasi-static framework that allows for stable interactions of asynchronously computed deformable objects. In the proposed approach, a deformable object can be computed at high refresh rates, while the remaining deformable virtual objects remain computed at low refresh rates. Moreover, contacts and other constraints between the different objects of the virtual environment are accurately solved using a shared Linear Complementarity Problem (LCP). Finally, we demonstrate our method on two test cases: a snap-in example involving non-linear deformations and a virtual thread interacting with a deformable object

Connective Tissues Simulation on GPU

International audienceRecent work in the field of medical simulation have led to real advances in the mechanical simulation of organs. However, it is important to notice that, despite the major role they may have in the interaction between organs, the connective tissues are often left out of these simulations. In this paper, we propose a model which can rely on either a mesh based or a meshless methods. To provide a realistic simulation of these tissues, our work is based on the weak form of continuum mechanics equations for hyperelastic soft materials. Furthermore, the stability of deformable objects simulation is ensured by an implicit temporal integration scheme. Our method allows to model these tissues without prior assumption on the dimension of their of their geometry (curve, surface or volume), and enables mechanical coupling between organs. To obtain an interactive frame rate, we develop a parallel version suitable for to GPU computation. Finally we demonstrate the proper convergence of our finite element scheme