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
More filters
Simulating Realistic Synthetic Longitudinal Brain MRIs with known Volume Changes
TL;DR: A novel way of reproducing realistic intensity variation in longitudinal brain MRIs is proposed, which is inspired by an approach used for the generation of synthetic cardiac sequence images, and combines a deformation field obtained from the biophysical model with a deformed field obtained by a non-rigid registration of two images.
Journal ArticleDOI
Medical Imaging Informatics: Towards a Personalized Computational Patient
TL;DR: Medical Imaging Informatics has become a fast evolving discipline at the crossing of informatics, Computational Sciences, and Medicine that is profoundly changing medical practices, for the patients' benefit.
Journal ArticleDOI
Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology
TL;DR: The 12 papers in this special issue focus on medical imaging and image computing in computational physiology and various aspects of the cardiovascular system across various physiological processes and observational scales.
Posted Content
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
TL;DR: In this article, a deformation-based framework is proposed to jointly model the influence of aging and Alzheimer's disease on the brain morphological evolution, which can be used to generate plausible morphological trajectories associated with the disease.
Book ChapterDOI
Topological Classification in Digital Space
TL;DR: A new approach to segment a discrete 3D object into a structure of characteristic topological primitives with attached qualitative features, which is stable to rigid transformations (rotations and translations) and stable to partial occlusions and local modifications of the object structure.