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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Journal ArticleDOI
Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images
Adityo Prakosa,Maxime Sermesant,Hervé Delingette,Stephanie Marchesseau,Eric Saloux,Pascal Allain,Nicolas Villain,Nicholas Ayache +7 more
TL;DR: A new approach for the generation of synthetic but visually realistic time series of cardiac images based on an electromechanical model of the heart and real clinical 4-D image sequences is proposed by combining three steps.
Journal ArticleDOI
Fast parameter calibration of a cardiac electromechanical model from medical images based on the unscented transform.
TL;DR: A fast automatic calibration method of the mechanical parameters of a complete electromechanical model of the heart based on a sensitivity analysis and the Unscented Transform algorithm is proposed, designed to replace manual parameter estimation as well as initialization steps that precede automatic personalization algorithms based on images.
Book ChapterDOI
Maximum Likelihood Estimation of the Bias Field in MR Brain Images: Investigating Different Modelings of the Imaging Process
TL;DR: This article proposes a third models for bias field correction in MR brain images and shows that for these three models, it is possible to use a common estimation framework, based on the Maximum Likelihood principle.
Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images
TL;DR: In this paper, a fully automatic algorithm is presented for the automatic segmentation of gliomas in 3D MR images, which builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classi cation of the volume.
Book ChapterDOI
Non-rigid Atlas to Subject Registration with Pathologies for Conformal Brain Radiotherapy
Radu Stefanescu,Olivier Commowick,Grégoire Malandain,Pierre-Yves Bondiau,Nicholas Ayache,Xavier Pennec +5 more
TL;DR: This work has created a parallel implementation of warping a digital atlas toward a patient image that can be used from the clinical environment through a grid interface and allows to locally control the amount of regularization.