N
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.
Papers
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Book ChapterDOI
Tracking Brain Deformations in Time-Sequences of 3D US Images
TL;DR: A feasibility study of a tracking tool based on intra-operative 3D ultrasound (US) images is presented, of great interest for the development of innovative and low-cost image guided surgery tools.
Journal ArticleDOI
Big Data and Artificial Intelligence in Vascular Surgery: Time for Multidisciplinary Cross-Border Collaboration
Fabien Lareyre,Christian-Alexander Behrendt,Arindam Chaudhuri,Nicholas Ayache,Hervé Delingette,Juliette Raffort +5 more
TL;DR: While international registries bring great perspectives to enhance knowledge on the management and the outcomes of patients with vascular diseases worldwide, they mainly focus on clinical and administrative data.
Journal Article
A novel framework for the 3D analysis of spine deformation modes.
TL;DR: The proposed method successfully extracted important deformation modes from a set of 3D spine models and can be used to refine arbitrary classes (King's or Lenke's classes, for instance), thus helping the design of new clinically relevant 3D classifications.
Proceedings ArticleDOI
Fully automatic registration of 3D cat-scan images using crest lines
TL;DR: A new technique to perform automatically the 3D registration of two 3D images based on crest lines and is fully automatic because crest lines are a very compact representation of the geometric information of the3D image.
Disentangling the normal aging from the pathological Alzheimer's disease progression on cross-sectional structural MR images
TL;DR: A method based on non-rigid registration is proposed to estimate the contribution of biological processes such as the normal aging and the AD-speci c pathological matter loss, and to identify the brain structural changes which are speci c for the pathological component of Alzheimer's disease.