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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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Journal ArticleDOI
Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology.
Sarah Montagne,Dimitri Hamzaoui,Alexandre Allera,Malek Ezziane,Anna Luzurier,Raphaelle Quint,Mehdi Kalai,Nicholas Ayache,Hervé Delingette,Raphaële Renard-Penna +9 more
TL;DR: In this paper, the variability of manual prostate zonal segmentation on T2-weighted (T2W) images was evaluated based on anatomical and morphological variation of the prostate (volume, retro-urethral lobe, intensity contrast between zones, presence of a PI-RADS lesion), variation in image acquisition (3D vs 2D T2W images), and reader's experience.
Proceedings Article
Evaluating Brain Anatomical Correlations via Canonical Correlation Analysis of Sulcal Lines
TL;DR: A new method to analyze structural brain correlations based on a large set of cortical sulcal landmarks, including maps of covariation between corresponding structures in opposite hemispheres, which show different degrees of hemispheric specialization.
Proceedings ArticleDOI
An efficient locally affine framework for the registration of anatomical structures
TL;DR: A general locally affine registration framework, which allows us to register local areas in the images using affine transformations having few degrees of freedom, and ensures a smooth, coherent and invertible transformation all over the image.
Journal ArticleDOI
Predicting PET-derived myelin content from multisequence MRI for individual longitudinal analysis in multiple sclerosis.
Wen Wei,Wen Wei,Emilie Poirion,Benedetta Bodini,Matteo Tonietto,Stanley Durrleman,Olivier Colliot,Bruno Stankoff,Nicholas Ayache +8 more
TL;DR: A method to predict the parametric map of [11C]PIB PET, from which the myelin content changes in a longitudinal analysis of patients with MS are derived, and this approach is shown to outperform the state-of-the-art methods qualitatively and quantitatively.