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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.

Papers
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Book ChapterDOI

Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

TL;DR: A sparse Bayesian kernel-based regression (relevance vector machine, RVM) from a large patient-specific simulated database is proposed for estimating cardiac activation maps and demonstrates the usefulness of this non-linear approach.
Patent

Method and System for Measuring the Speed of Blood Flow

TL;DR: In this paper, a method for measuring the speed of a red blood cell moving inside a flow such as a flow of the blood, using a light scanning microscope, was proposed. But the method was not suitable for the detection of the red blood cells.
Book ChapterDOI

Characterization of Post-infarct Scars in a Porcine Model --- A Combined Experimental and Theoretical Study

TL;DR: The main purpose of this work was to characterize the infarct scars using in vivo electro-anatomic CARTO maps (recorded in sinus rhythm) and high-resolution ex-vivo MR images in a porcine model of chronicinfarct.

FLAIR MR Image Synthesis By Using 3D Fully Convolutional Networks for Multiple Sclerosis

TL;DR: 3D fully convolutional neural networks are proposed to predict a FLAIR MRI pulse sequence from other MRI pulse sequences and both the qualitative and quantitative results show that the method is competitive for FLAIR prediction.
Journal ArticleDOI

Applications of artificial intelligence in cardiovascular imaging.

TL;DR: In this paper, the authors discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems.