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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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Journal ArticleDOI

Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging

TL;DR: Results show that detecting important structures such as the ventricles and brain outlines greatly improves the results and a method that incorporates prior anatomical knowledge in the shape of digital atlases that deform to fit the image data to be analysed.
Patent

System and Method for Patient Specific Modeling of Liver Tumor Ablation

TL;DR: In this paper, a method and system for liver tumor ablation planning and guidance based on a patient-specific model of liver tumor ABlation is disclosed, which is estimated from 3D medical image data of a patient.
Patent

Method and device for examining a body, particularly for tomography

TL;DR: In this article, the authors defined an ordered set of gross data signals, each representing the intensity of a wave beam such as X rays, which comes out of the body along an axis called "acquisition line", which axis is designated by at least two parameters.

An 0(n2) algorithm for 3D substructure matching of proteins

TL;DR: A new 3D substructure matching algorithm based on geometric hashing techniques that allows to compute for every couple of amino acids 6 invariants, and therefore drastically reduce the complexity of both the preprocessing and recognition stages of geometric hashing.
Book ChapterDOI

Preliminary validation using in vivo measures of a macroscopic electrical model of the heart

TL;DR: Validating a macroscopic model with in vivo measurements of the electrical activity should allow a future use of the model in a predictive way, for instance in radio-frequency ablation planning.