Équipe MLMS - Machine Learning, Modélisation et Simulation

Séminaires d'équipe

De Équipe MLMS - Machine Learning, Modélisation et Simulation
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Organisation

Les activités d'équipes ont lieu le mardi matin en salle de IASO à l'IHU.

Suivant les semaines nous pouvons avoir réunion des permanents à 10h, réunion d'équipes à 10h30 et un exposé à 11h.

Date des réunions parmanents à 10h : 15 février 2022, 8 mars 2022, 5 avril 2022, 3 mai 2022, 14 juin 2022, 5 juillet 2022.

Calendar (Only invited have access to the calender)

Organisateur

Michel Duprez


Réservation des salles

Meeting room 114

Meeting room 127

Conference room at IHU


Prochains séminaires / réunions

  • mardi 12 juillet 2022 : exposé de Valentina Scaponi à 11h

Titre : "Autonomous endovascular catheter navigation"

Résumé : "Endovascular diseases are one of the leading causes of death overall and among all procedures, endovascular interventions are one of the most performed. Only in the USA, this procedure is carried out more than one million times per year, involving different districts of the body, like the heart and the brain, the kidneys and the liver, etc. This procedure involves the insertion of thin tube devices, called guidewires and catheters, inside the patient’s vascular tree. These devices are then navigated toward the target location where they are used to treat or diagnose different types of pathologies. All along the procedure, fluoroscopic images are acquired to let the surgeon see the vessel’s geometry and to allow him to perform the navigation. To acquire these images, both the patient and the surgeon are exposed to x-radiations, which can cause cell damage potentially leading to the development of tumoral masses. The aim of this project is to develop a control algorithm, able to autonomously navigate both the catheter and the guidewire, reducing or even eliminating the exposure to x-radiations thanks to the use of FBGs sensors. This control algorithm will then be used as initial policy of a reinforcement learning algorithm."


  • mardi 19 juillet 2022 : réunion d'équipe à 11h


Séminaires / réunions passés

  • mardi 5 juillet 2022 : réunion permanent 10h et réunion d'équipe à 11h


  • mardi 21 juin 2022 : réunion permanent 10h et exposé de Diwei Wang + réunion d'équipe à 11h

Titre : "Dementia detection in gait videos based on 3D human pose estimation"

Résumé : "Dementia with Lewy bodies and Alzheimer’s disease are two common neurodegenerative diseases among elderly people. They often cause motor impairments such as tremor at rest, rigidity, bradykinesia, and postural instability. Gait analysis is frequently used in clinical applications to detect these anomalies. However, assessments relying on wearable sensors are costly, and sometimes intrusive. Commercial 3D motion analysis systems require carefully calibrated cameras to collect multi-view video data, and are thus not practical. Therefore, we are focused on estimating dementia type and severity using monocular gait videos only. By adopting anomaly mesure-dependent feature learning, and considering the problem of data imbalance, we propose a two-phase training mechanism based on a 3D human pose estimation model. In this presentation, I will explain the approach, present our gait dataset, and show the current problems in modeling temporal correlations between frames."


  • mardi 24 mai 2022 : exposé d'Emmanuel Franck (IRMA) à 11h


Titre : "Scientific machine learning: some applications"

Résumé : "Scientific computing, at the interface between numerical analysis, PDE and HPC, has been used for many years to build high-performance simulation codes for physics, biology and medicine. Recently, a new branch, called scientific ML, has emerged. It allows to couple standard scientific computing approaches with deep learning methods. In this presentation, after a quick general presentation, we propose to illustrate this concept through several examples. In a first step, we will focus on the design of reduced models for PDEs from physics. Then we will propose examples concerning the optimization of numerical methods for PDEs. Finally, a control problem from biology will be introduced."


  • mardi 17 mai 2022 : réunion d'équipe et exposé de Pierre Galmiche à 11h

Titre : "Towards a modelization of breast evolution during radiotherapy"

Résumé : "Breast cancer is usually treated using radiotherapy after a conservative surgery.

Current methods used to irradiate patients are based on the hypothesis that breast shape and volume don't change during therapy.
This hypothesis has been questioned by some radiotherapists, observing volume and surface changes on patients.
Knowing this, we worked on an approach to track the evolution of shape and volume of breasts during radiotherapy using clinical trial data from the ICANS institute.
During this presentation, I will present our approach, show our results concerning the partial shape matching problem and highlight some difficulties encounted while working on real data."


  • mardi 3 mai 2022 : réunion permanent 10h et réunion d'équipe à 11h


  • mardi 19 avril 2022 : réunion d'équipe et exposé de Boyang Yu à 11h

Titre : "Differentiable Cloth Simulation and Inverse Simulation"

Résumé : "Cloth simulation attracts the attention of many researchers because of its numerous applications in 3D content production such as movies and games. However, classic cloth animation requires expertise and practice, sometimes it may take many attempts to achieve the desired effect. This makes the idea of differentiable cloth simulation appealing. With a proper loss function and by applying gradient-based optimization methods, It can enable automatic control of physical systems or parameter estimation. In this presentation, we will see how to make a physical cloth simulation pipeline differentiable so that the gradients could be backpropagated through it."


  • mardi 5 avril 2022 : réunion permanent 10h, réunion d'équipe et exposé de Guillaume Mestdagh à 11h

Titre : "An optimal control problem for elastic registration and force estimation in augmented surgery"

Résumé : "The nonrigid alignment between a pre-operative biomechanical model and an intra-operative observation is a critical step to track the motion of a soft organ in augmented surgery. While many elastic registration procedures introduce artificial forces into the direct physical model to drive the registration, we propose in this paper a method to reconstruct the surface loading that actually generated the observed deformation. The registration problem is formulated as an optimal control problem where the unknown is the surface force distribution that applies on the organ and the resulting deformation is computed using an hyperelastic model. Advantages of this approach include a greater control over the set of admissible force distributions, in particular the opportunity to choose where forces should apply, thus promoting physically-consistent displacement fields. The optimization problem is solved using a standard adjoint method. We present registration registration results with experimental phantom data showing that our procedure is competitive in terms of accuracy. In an example of application, we estimate the forces applied by a surgery tool on the organ. Such an estimation is relevant in the context of robotic surgery systems, where robotic arms usually do not allow force measurements, and providing haptic feedback remains a challenge."


  • mardi 22 mars 2022 : réunion d'équipe et exposé de François Lecomte à 11h

Titre : "CNN-based Diffeomorphic Mapping for Real-Time Deformable 2D-3D registration"

Résumé : "We present a method for estimating, in real-time, a 3D displacement field from a single fluoroscopic image. Our approach uses a fully convolutional network architecture to solve the associated inverse problem. Supervised learning is performed on synthetic data, using Digitally Reconstructed Radiographs as input and displacement fields as output. These displacement fields are generated using a diffeomorphic mapping framework to enforce invertibility and smoothness. This also allows our method to be generic and independent of a particular organ deformation or surgical procedure, although it can be tuned to follow the key characteristics of a specific scenario. Results show that our method can estimate the 3D displacement field with an average accuracy of 0.24 ± 0.29mm, at a frame rate of about 20 frames persecond.


  • mardi 8 mars 2022 : réunion permanent 10h et exposé de Saurabh Deshpande (Luxembourg) à 11h

Titre : "Probabilistic Deep Learning for Real-Time Large Deformation Simulations"

Résumé : "In this work, we propose a probabilistic deep learning surrogate framework that is capable of accurately predicting non-linear deformations of bodies together with the predictions’ uncertainties. The framework directly takes the Finite Element nodal forces at the neural network input to give nodal displacements at its output. The probabilistic part of the framework is based on a dedicated Variational Inference formulation, thanks to which we are not only able to efficiently capture uncertainties related to noisy data, but we have also knowledge about the model uncertainties—which is especially important in regions not well supported by the data (e.g., the extrapolated region)."


  • mardi 22 février 2022 : réunion d'équipe à 11h


  • mardi 15 février 2022 : réunion permanent 10h et exposé à 11h de Leo Nouveau

Titre : "High order embedded strategy for elliptic PDEs in mixed form with the Shifted Boundary Method."

Résumé : "This presentation will be divided in two parts. First, we will see the main features of the Shifted Boundary Method, introduced by A. Main and G. Scovazzi [1]. This method aims at imposing the boundary condition known on an immersed boundary, onto a surrogate one, composed by edges/surfaces of the underlying mesh. To account for the discrepancy between the “true” and “shifted” boundaries, the method modifies the imposed boundary value using Taylor expansions. This allows to preserve the accuracy of the underlying finite element scheme. In a second part dedicated to elliptic PDEs in mixed form, we will investigate how on P1 elements, an overall second order accuracy can be recovered for both Neumann and Dirichlet conditions. This is done using the enrichment of the primary variable [2], and also allows to obtain a third order accurate scheme when only Dirichlet conditions are embedded [3]. This part will be concluded by on-going work, with T. Carlier, H. Beaugendre and M. Colin, for an application to the Stefan problem with moving front."


  • mardi 8 février 2022 : réunion d'équipe à 11h


  • mardi 1er février 2022 : pas de réunion


  • mardi 25 janvier 2022 : réunion d'équipe 10h, exposé à 11h de Pierre Mollo

Titre : "Modeling cerebral venous blood flows"

Résumé : "Several factors make venous blood flows much less studied than their arterial counterparts. For example, the venous tree structure presents great variation from one individual to another: preponderance of certain structures, asymmetry, absence of other structures. This makes a generic study very complex. However, we will see in this presentation that by limiting ourselves to cerebral blood flows, we can empirically identify groups of individuals. Moreover, the restriction to the intracranial compartment allows us to use the hypothesis of incompressibility of venous blood vessels. Thus we will see and compare several models for these flows but also their possible extensions. Finally, we will discuss the possibility of using reduced models and the associated applications."


  • mardi 18 janvier 2022 : pas de réunion


  • mardi 11 janvier 2022 : réunion permanent à 10h et réunion d'équipe à 11h


  • mardi 4 janvier 2022 : pas de réunion


  • mardi 21 décembre 2021 : réunion d'équipe à 11h


  • mardi 14 décembre 2021 : pas de réunion


  • mardi 7 décembre 2021 : réunion permanent à 10h, réunion d'équipe à 11h


  • mardi 9 novembre 2021 : Muhammad Sajjad

Titre : "Efficient Deep Learning Methods for IoT Applications: Current Challenges and Future Directions"


  • mardi 26 octobre 2021 : Eligiusz Postek

Titre : "A concept of a coupled agent-stress mechanical model of a tissue"

Résumé : "The physical environment of living cells and tissues, and more particularly their mechanical interaction with it, plays a crucial regulatory role in their biological behavior such as cel differentiation, apoptosis, proliferation, tissue growth, remodeling, wound healing, etc. We will use a concept of coupling of the Agent Based Modelling (ABM) and mechanical modeling. The latter will be applied to single cell models and their colonies. Therefore, it is important to evaluate state of stress in the growing, evolving tissue. To do this, a model of a single cell is necessary as well. The single cell model should consist of the cytoskeleton, cytoplasm, nucleus and cortex."


  • mardi 31 août 2021 : Boyang Yu et zhiyu Zheng

Titre de Boyang Yu : "SOTA cloth modeling and its use in motion simulation"

Résumé de Boyang Yu : "The animation of digital humans in clothing has numerous applications in 3D content production. However, cloth modeling requires editing the garment shape in 2D, manually placing, and fine-tuning to achieve final results. This pipeline does need expertise even for an experienced 3D animator. To facilitate cloth modeling, some data-based work has been proposed to learn efficient approximate models, the application of deep learning in recent years has promoted it further. Besides, the reconstruction of humans in clothing is also a topic of interest to researchers. By fitting 3D human & cloth models to a sequence of 3D scans or a video, a more tidy sequence of meshes could be retrieved. In this context, we would like to propose an efficient yet independent of body modeling of cloth, it should be compatible with existing cloth simulator and well parameterized for learning."

Titre de zhiyu Zheng : "On the differentiable cloth simulator for inverse problem"

Résumé de zhiyu Zheng : "This project is to explore a differentiable cloth simulator to see its performance for inverse problem like motion control and external force estimation. In our project, we have studied the differentiable cloth simulator coupled with deep neural networks. Firstly, we have tested the performance of the simulator with motion control task, and we found that the simulator coupled with the neural networks can be trained to execute different tasks of throwing the piece of cloth into the basket at different locations. Our result only provides a view of the possibility for more extended applications, and it could be further optimized with more trainings. Then we test its performance for dropping force estimation, we found that given a sequence of cloth mesh frames, the simulator can well determine the dropping force according to their deformation and movement. This is important and could be potentially applied for more complicated external force estimation like the force applied by the human body on the cloth."


  • mardi 13 juillet 2021 : Anne-Sarah Debly

Titre : "Autonomous endovascular navigation using reinforcement learning"

Résumé : "Catheters are mainly used to treat cardiac diseases like strokes and heart attacks but navigating through the vascular tree is a hard task for the surgeon. Today, he can follow the position of the catheter in the patient's vessels thanks to fluoroscopic imaging, but it gives only 2D images, with a complex visualization of the vessels. For these reasons, we propose to develop an estimation of the 3D shape of the catheter in 3D thanks to FBG sensors placed on an optic fiber to retrieve the 3D shape of the device. Then, we would like to develop a closed-loop system based on depp reinforcement learning to make possible the autonomous navigation of the catheter. By combining knowledge of the 3D shape of the catheter from the sensors, and a deep learning algorithm to choose the best path to the target, a robot could control the catheter tip."

Enregistrement : bbb

Slides : pdf


  • mardi 29 juin 2021 : Virginie Marec

Titre : "Integration of a force sensor for online estimation of the behaviour of the system and parametrization of the real-time simulation during robotic needle insertion."

Résumé : "This internship focuses on real-time updates of a biomechanical model of a needle insertion procedure based on force sensor data. In fact, a force sensor can be integrated at the base of a needle in order to measure in real time the force applied to the latter during its insertion. These data will then make it possible to achieve 2 objectives: -Detect layer changes when inserting the needle in a heterogeneous gel (skin, fat, muscle, liver). -Estimate the frictional forces applied along the needle. In parallel, a finite element model is implemented on SOFA. We want to obtain a realistic simulation that correctly models the behavior and the deformations of the tissues during the insertion of the needle. The final objective of this project is to create a method which, from the values ​​obtained from a force sensor, is able to deduce the state of the system (i.e. in which tissue is the needle and how friction acts on it). The parameters of the SOFA model can then be adjusted in real time so that the simulation best matches reality."

Enregistrement : bbb

Slides : pdf


  • mardi 22 juin 2021 : Josephine Riedinger


Titre : "Closed-loop Transcranial Electric Stimulation of Neural Networks in a Rodent Model of Psychotic Transition"

Résumé : "Schizophrenia is a psychotic disorder characterized by a loss of contact of the patient with reality. The prodromal phase is the period in which some symptoms happened and announced the onset of the disease. Today, there is an increasing interest in preventing the psychotic transition of patients-at-risk (in the prodromal phase) to the chronic psychotic phase of the disease. Interestingly, it was shown that patients in this prodromal phase shown abnormal cerebral oscillations that can be used as biomarkers. Hence, we can ask if the normalization of these "oscillopathies" could delay, or even prevent, the psychotic transition and so, the entry of the patients in the chronic phase of the disease. One way could be the application of a neuromodulation technique, such as the promising Transcranial Electrical Stimulation (TES). In this pre-clinical project, we would like to explore if a closed-loop TES application can normalize the oscillopathies found in patients. For this purpose, electrophysiological experiments are conducted in an animal model of ketamine-induced psychotic transition. Complementary, a model of the brain network of interest is designed and an adapted control system is researched. An extended Kalman filter will allow the model observer to take into account real brain activity for the computation of the predictions of states and observations. Advancements and first results are encouraging and will be presented."

Enregistrement : bbb


  • mardi 15 juin 2021 : Thomas Wahl et Philippe Pincon

Titre de Thomas Wahl : "Effects of drugs and neurostimulation on gamma-oscillations in neural networks."

Résumé de Thomas Wahl : "Psychosis in schizophrenia is known to be correlated with a stronger activity in the frequency range 25-60 Hz, corresponding to γ-oscillations in electroencephalograms (EEGs). Neurostimulation can be used to attenuate this effect. By using a mean-field description derived from a network of interacting excitatory and inhibitory neural population with additive noise input, we can apply linear response theory to study analytically the properties of γ-oscillations, to explain the emergence of noise driven oscillations, also known as quasi-cycles. We can use our understanding of the properties of quasi-cycles to show in details the influence of our model parameters on the properties of the γ-oscillations."

Titre de Philippe Pincon : "Effects of drugs and neurostimulation on gamma-oscillations in neural networks."

Enregistrement : bbb


  • mardi 8 juin 2021 : Rongrong Liu

Titre : "Wearable Sensor Technology for Individual Grip Force Profiling."

Résumé : "Wearable biosensor systems with transmitting capabilities represent innovative technology developed to monitor exercise and other task activities. This technology enables real-time, convenient, and continuous monitoring of a user’s behavioral signals, relative to body motion, body temperature and a variety of biological or biochemical markers, like individual grip force, which is studied here. To achieve this goal, a four-step pick-and-drop image-guided robot-assisted precision task has been designed using a wearable wireless sensor glove system. The spatio-temporal grip force profiling is analyzed on the basis of thousands of individual sensor data collected from the twelve locations on the dominant and non-dominant hands of each of the three users in ten successive task sessions. Statistical comparison has shown specific differences between the grip force profiles of individual users as a function of task skill level and expertise."

Enregistrement : bbb

Slides : pdf


  • mardi 25 mai 2021 : Pierre Galmiche

Titre : "The Functional representation of the shapes."

Résumé : "Breast cancer can be treated using radiotherapy after a conservative surgery. The current method used to irradiate patients is based on the hypothesis that the breast shape and volume don't change across radiotherapy sessions. This hypothesis has been questioned by some radiotherapists, observing volume and surface changes during therapy. Knowing this, we want to track the deformation across radiotherapy using clinical trial data from the ICANS institute. To answer this problematic we chose to focus on the Spectral Representation of the shapes to solve the Shape Matching Problem."

Enregistrement : bbb

Slides : pdf


  • mardi 18 mai 2021 : Paul Baksic

Titre : "Shared control strategy for �needle insertion into deformable tissue using inverse Finite Element simulation"

Résumé : "We have previously proposed a fully automated strategy for percutaneous procedures. This algorithm is relying on a FEM simulation used to derive the Jacobian linking the needle base motion to its tip motion relative to the target (the tumor) while taking into account needle-tissue interaction. This jacobian is used to compute the motion of the robot holding the needle needed to reach the target. To compute it, a FE model of the liver is registered using 3D positions of a small set of points in the liver measured in real-time. But to do so, it is assumed that those 3D positions can be measured in real-time, which is currently not possible. In our recent work, we propose a more realistic shared-control framework where only 2D information of those points, coming from a C-ARM, are used to register the model. This introduces higher registration errors that cannot be compensated by the automatic algorithm. That is why we have proposed a new shared control strategy for needle insertion into soft tissue. It consists in leaving the decision-making part (when to insert the needle, and where should the tip go inside of the tissue) to the practitioner. This is done through a haptic interface, throughout which the user controls the target of the automatic algorithm inside of the tissue. In addition, the user is guided on a predefined path using virtual fixtures. Alongside this haptic interface, augmented 2D fluoroscopic images are provided to the practitioner. This allows leaving the complex motion of the needle base needed to take the needle-tissue interaction into account to the automated algorithm, while the user can focus more on the important part which is, where to place the needle tip. This method an the results of a user study published at ICRA2021 are presented here. In addition, efforts are made to go from a simulated environment to a real phatom trial. This is still a work in progress and the last advances are presented here. "

Enregistrement : bbb


  • mardis 4 et 11 mai 2021 : Cedric Bobenrieth

Titre : "Modélisation Géométrique par Croquis"

Résumé : "De nos jours, la modélisation 3D est omniprésente, cependant les outils modernes pour créer des modèles 3D sont complexes et requièrent beaucoup de temps. A contrario, l’esquisse est un moyen naturel de communiquer rapidement des idées, ainsi une méthode permettant la reconstruction automatique d’objets 3D à partir d’un croquis simplifierait ce processus. Cette méthode devrait résoudre deux problèmes : le calcul des parties cachées de la forme dessinée et la détermination des coordonnées 3D à partir des données 2D du croquis. Dans cette présentation, je vous parlerais de deux nouvelles approches qui visent à surmonter ces problèmes. La première se sert d’a priori et d’une base de données préexistantes pour permettre la reconstruction 3D automatique de fleurs à partir d’un seul croquis selon n’importe quel angle de vue. La seconde permet la reconstruction de tout type d’objets, sans limitations, en utilisant un style de dessin plus informatif et en étant guidée par l’utilisateur."

Enregistrement : bbb

Slides : pdf


  • mardi 27 avril 2021 : Dawood Al Chanti

Titre : "IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscles Segmentation and Propagation in 3-D Freehand Ultrasound"

Résumé : "We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. To this end, we propose a deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices and uses it to propagate a reference mask annotated by a clinical expert. To handle longer changes of the muscle shape over the entire volume and to provide an accurate propagation, we devised a Bidirectional Long Short Term Memory module. To train our model with a minimal amount of training samples, we propose a strategy to combine learning from few annotated 2D ultrasound slices with sequential pseudo-labeling of the unannotated slices. To promote few-shot learning, we propose a decremental update of the objective function to guide the model convergence in the absence of large amounts of annotated data. Finally, to handle the class-imbalance between foreground and background muscle pixels, we propose a parametric Tversky loss function that learns to adaptively penalize false positives and false negatives. We validate our approach for the segmentation, label propagation, and volume computation of the three low-limb muscles on a dataset of 44 subjects. We achieve a dice score coefficient of over 95% and a small fraction of error with 1.6035%."

Enregistrement : bbb


  • mardi 20 avril 2021 : Mathieu Naudin

Titre : "Virtual biopsy"

Résumé : "Virtual biopsy is a real societal issue from screening to diagnosis through patient follow-up. It is defined as allowing, from a set of data, to propose a diagnosis and to link it to an uncertainty. The goal is to one day consider the disappearance of physical biopsy for obvious reasons: invasive, traumatic and sometimes dangerous or unfeasible. This method requires an important mix of expert knowledge and artificial intelligence. It uses formatted measurements as an entry point, and neural networks specific to the desired diagnosis and thus respond to the medical problem as a whole. We will see the implementation of this approach through some of my thesis work as well as my recent research on the premises of the following axes: digital twin, lesion microenvironment and deployment in real conditions."


  • mardi 13 avril 2021 : Guillaume Mestdagh

Titre : "An optimal control approach for surface-matching in augmented surgery"

Résumé : "Augmented surgery consists in providing in real-time a 3D view of an organ during a surgical intervention. In this context, the displacement field in the organ is reconstructed from partial data. We propose an optimal control approach for this problem, involving an elastic model describing the organ's response to surface loadings and a functional measuring the discrepancy between the current displacement field and available data. In this formulation, we try to reconstruct a physically plausible surface loading field rather than create artificial forces to generate a displacement. In this presentation, we introduce the optimal control formulation and discuss its advanges, and then we show a numerical example on a toy problem."

Slides : pdf

Enregistrement : bbb


  • mardi 30 mars 2021 : Ziqiu Zeng

Titre : "Method of isolating dofs on dealing with contact equtations"

Résumé : "The construction of compliance matrix is usually a main obstacle in real-time FE simulation with interactions between objects. Recently we have developed a new GPU-based method to solve the contact equations using LDL factorization and nested dissection. The approach significantly reduces the computation cost for contact resolution. In the meeting I will present our cmputation strategy as well as our latest results."


  • mardi 23 mars 2021 : Hyewon Seo

Titre : "Dynamic skin deformation prediction by recurrent neural network"

Résumé : "Skin dynamics contributes to the enriched realism of human body models in rendered scenes. Traditional methods rely on physics-based simulations to accurately reproduce the dynamic behavior of soft tissues. Due to the model complexity and thus the heavy computation, however, they do not directly offer practical solutions to domains where real-time performance is desirable. The quality shapes obtained by physics-based simulations are not fully exploited by example-based or more recent data-driven methods neither, with most of them having focused on the modeling of static skin shapes by leveraging quality data. To address these limitations, we present a learning-based method for dynamic skin deformation. At the core of our work is a recurrent neural network that learns to predict the nonlinear, dynamics-dependent shape change over time from pre-existing mesh deformation sequence data. Our network also learns to predict the variation of skin dynamics across different individuals with varying body shapes. After training the network delivers realistic, high-quality skin dynamics that are specific to a person in a real-time course. We obtain results that significantly saves the computational time, while maintaining comparable prediction quality compared to state-of-the-art results."

Slides : pdf

Enregistrement : bbb


  • mardi 16 mars 2021 : François Lecomte

Titre : "Recalage 3D/2D grâce au Deep Learning sur des données synthétiques"

Résumé : "Cette présentation parlera du recalage de données CT sur des images fluoroscopiques. Je présenterai notre processus de génération de données synthétiques (DRRs) à partir d’un CT pré-opératoire. J’exposerai ensuite brièvement l’architecture du réseau et le processus d’apprentissage. Après un résumé du processus complet, je présenterai nos résultats sur le dataset de validation synthétique. Je conclurai sur les limites actuelles et les prochaines étapes dans le développement de la méthode."

Slides : pdf

Enregistrement : bbb


  • mardi 9 mars 2021 : Robin Enjalbert

Titre : "Automatic catheter navigation through deep reinforcement learning"

Résumé : "For this meeting, I will introduce you the basics of Deep Reinforcement Learning as well as a specific algorithm, Deep Q-Network. Then, I will present my work on Deep Reinforcement Learning for the navigation of endovascular catheters."

Slides : pdf

Enregistrement : bbb


  • mardi 2 mars 2021 : Axel Hutt

Titre : "Models of drug and stimulation impact on neural populations"

Résumé : "The talk shows recent mathematical and numerical results on the drug impact and stimulation impact on brain models. The effects are considered in the context of cognition, general anaesthesia and mental disorders."

Slides : pdf

Enregistrement : bbb


  • mardi 23 février 2021 : Sergei Nikolaev

Titre : "Parameters estimation using Kalman filters for predictive simulation"

Résumé : "In this talk, we are going to talk about Kalman filters. Firstly, there will be a brief introduction. Then we will see several options to reduce the order of the filter, in order to obtain the data assimilation process close to real-time. Finally, several examples will be presented."

Slides : pdf

Enregistrement : bbb


  • mardi 16 février 2021 : Jean-Nicolas Brunet

Titre : "Creating python bindings with pybind11 and SofaPython33"

Résumé : "During this meeting, we will go through the basics of creating packages and modules in python, and how they are automatically found by python. We will then learn how to create packages and modules in C++ with pybind11. Finally, we will go through the bindings of a simple SOFA plugin using both pybind11 and SofaPython3. We will see how we can call C++ functions from our components directly in python, how to inherits a c++ class from a python class and how to bind specific SOFA data types in python."

Enregistrement : youtube

Code source du tutoriel : https://github.com/jnbrunet/tutorial_sp3

Exemple SofaOffscreenCamera : https://github.com/jnbrunet/SofaOffscreenCamera


  • mardi 2 février 2021 : Michel Duprez

Titre : "φ-FEM: une méthode éléments finis aux frontières immergées sur des domaines définis par une fonction level-set"

Résumé : Les méthodes éléments finis classiques utilisent des maillages qui coïncident avec le bord et les interfaces du domaine sur lequel nous effectuons la simulation numérique. Suivant le type d'éléments du maillage utilisés ou lorsque la géométrie du domaine est trop complexe, une méthode alternative consiste à effectuer les calculs sur un maillage qui ne coïncident pas avec le bord et les interfaces du domaine. φ-FEM appartient à cette classe de techniques et a la particularité de tenir compte des forces externes à l'aide d'une fonction "level-set" qui s'annule au bord. Dans cet exposé, je rappellerai tout d'abord la méthode des éléments finis "classique" et quelles contraintes géométriques doivent satisfaire les maillages. Je présenterai ensuite les différentes méthodes aux frontières immergées précédentes, leurs avantages et leur inconvénients. Enfin, j'introduirai φ-FEM, quel verrous scientifique cette méthode permet de lever et dans quel cadre elle a été développée pour le moment. Je terminerai par quelques simulations numériques.

Slides : pdf