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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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Model-Based Multiscale Detection of 3D Vessels

TL;DR: In this article, a multiscale analysis is used to extract the vessel network surrounding an aneurysm from 3D angiography of the brain, and a smoothed skeleton of the vessels is combined with a MIP or a volume rendering to enhance their visualization.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as discussed by the authors was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Book ChapterDOI

Simulating Patient Specific Multiple Time-Point MRIs from a Biophysical Model of Brain Deformation in Alzheimer’s Disease

TL;DR: A biophysical model of brain deformation that can generate biologically plausible deformation for any given desired volume changes at the voxel level of the brain MRI is used to simulate patient specific structural Magnetic Resonance Images from the available MRI scans of Alzheimer's Disease subjects.
Book ChapterDOI

In vivo contact EP data and ex vivo MR-based computer models: registration and model-dependent errors

TL;DR: Small errors are found between the measured and the predicted activation times, as well as between the depolarization times using these three models, suggesting that simple mathematical formalisms might be a good choice for integration of fast, predictive models into clinical platforms.