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Nicholas Ayache
Researcher at French Institute for Research in Computer Science and Automation
Publications - 639
Citations - 47063
Nicholas Ayache is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Segmentation & Image registration. The author has an hindex of 97, co-authored 624 publications receiving 43140 citations. Previous affiliations of Nicholas Ayache include University of Las Palmas de Gran Canaria & Mauna Kea Technologies.
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Journal ArticleDOI
Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins
TL;DR: A novel method is proposed for estimating the full extent of the tumor infiltration starting from its visible mass in the patients' MR images and it is suggested that the variable margin could be more effective at targeting cancerous cells and preserving healthy tissue.
Book ChapterDOI
Real Time Volumetric Deformable Models for Surgery Simulation
TL;DR: A virtual environment for surgical training and more specifically a model based on elasticity theory which conveniently links the shape of deformable bodies and the forces associated with the deformation while achieving real time performance is described.
Journal ArticleDOI
Medical computer vision, virtual reality and robotics
TL;DR: This paper proposes a list of such problems after a review of the current major 3D imaging modalities, and a description of the related medical needs, and presents some of the past and current work done in the research group EPIDAURE at INRIA on the following topics.
Journal ArticleDOI
Improving realism of a surgery simulator: linear anisotropic elasticity, complex interactions and force extrapolation
TL;DR: The latest developments of the minimally invasive hepatic surgery simulator prototype developed at INRIA are described, including the implementation of several biomechanical models and the integration of two force-feedback devices in the simulation platform.
Book ChapterDOI
Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets
TL;DR: The method is used to analyze the morphological evolution of 2D profiles of hominids skulls and to analyze brain growth from amygdala of autistics, developmental delay and control children.