A perception and manipulation system for collecting rock samples

An important part of a planetary exploration mission is to collect and analyze surface samples. As part of the Carnegie Mellon University Ambler Project, researchers are investigating techniques for collecting samples using a robot arm and a range sensor. The aim of this work is to make the sample collection operation fully autonomous. Described here are the components of the experimental system, including a perception module that extracts objects of interest from range images and produces models of their shapes, and a manipulation module that enables the system to pick up the objects identified by the perception module. The system was tested on a small testbed using natural terrain

Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs

International audienceWe perform unsupervised analysis of image-derived shape and motion features extracted from 3,822 cardiac Magnetic resonance imaging (MRIs) of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify 2 small clusters that probably correspond to 2 pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this finding. Moreover, we examine the differences between the other large clusters and compare our measures with the ground truth

Face cloning and video spatialization : tools for virtual teleconference

In this paper, we propose powerful virtual image processing tools (face cloning and video spatialization) which can be useful to design new teleconferencing systems offering a better comfort for users even if very low bit rate links are used . These tools allow a new teleconferencing concept, relying on the metaphor of a virtual meeting room where participants can choose their position and point of view. In particular, we propose video cloning modules to represent all participants vith 3D synthetic models of their face, constructed from range data with simplex meshes . These models are meant to be visualized under a point of view different from the camera which analyses the facial motion of the speakers . Besides, the realism of the virtual meeting room is improved by video spatialization techniques, which aims at synthesizing new points of view from a limited set of uncalibrated views of an existing room .Dans cet article, nous proposons des algorithmes de traitement d'image vidéo (tels que le clonage de visages et la spatialisation vidéo) qui peuvent être utilisés pour définir de nouveaux systèmes de vidéoconférence offrant plus de « confort d'utilisation » que les systèmes actuels, malgré des liaisons très bas-débit. Ce nouveau concept repose sur la métaphore d'une salle de réunion virtuelle où les utilisateurs pourront choisir leur place. En particulier, nous proposons des modules de clonage vidéo pour représenter les participants par l'intermédiaire de modèles synthétiques 3D de leur visage, obtenus par création de maillages simplexes sur des données Cyberware. Ces modèles sont visualisables sous des points de vue différents de celui de la caméra qui analyse les mouvements des participants. Par ailleurs, le réalisme de l'espace de réunion virtuelle est renforcé par des techniques de spatialisation vidéo qui a pour but de créer des points de vue inédits à partir d'images statiques non-calibrées d'une salle de réunion existante

Segmentation of nerve bundles and ganglia in spine MRI using particle filters

14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part IIIAutomatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.National Institutes of Health (U.S.) (NAMIC award U54-EB005149)National Science Foundation (U.S.) (CAREER grant 0642971

Anatomical Modelling of the Musculoskeletal System from MRI

Abstract. This paper presents a novel approach for multi-organ (mus-culoskeletal system) automatic registration and segmentation from clini-cal MRI datasets, based on discrete deformable models (simplex meshes). We reduce the computational complexity using multi-resolution forces, multi-resolution hierarchical collision handling and large simulation time steps (implicit integration scheme), allowing real-time user control and cost-efficient segmentation. Radial forces and topological constraints (at-tachments) are applied to regularize the segmentation process. Based on a medial axis constrained approximation, we efficiently characterize shapes and deformations. We validate our methods for the hip joint and the thigh (20 muscles, 4 bones) on 4 datasets: average error=1.5mm, computation time=15min.

DejaVu: Intra-operative Simulation for Surgical Gesture Rehearsal

International audienceAdvances in surgical simulation and surgical augmented reality have changed the way surgeons prepare for practice and conduct medical procedures. Despite considerable interest from surgeons, the use of simulation is still predominantly confined to pre-operative training of surgical tasks and the lack of robustness of surgical augmented reality means that it is seldom used for surgical guidance. In this paper, we present DejaVu, a novel surgical simulation approach for intra-operative surgical gesture rehearsal. With DejaVu we aim at bridging the gap between pre-operative surgical simulation and crucial but not yet robust intra-operative surgical augmented reality. By exploiting intra-operative images we produce a simulation that faithfully matches the actual procedure without visual discrepancies and with an underlying physical modelling that performs real-time deformation of organs and surrounding tissues, surgeons can interact with the targeted organs through grasping, pulling or cutting to immediately rehearse their next gesture. We present results on different in vivo surgical procedures and demonstrate the feasibility of practical use of our system