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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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Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy
TL;DR: An efficient algorithm to train neural networks for an end-to-end segmentation of multiple and nonexclusive classes is proposed, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes.
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Velocity-based cardiac contractility personalization from images using derivative-free optimization
Ken C. L. Wong,Maxime Sermesant,Kawal Rhode,Matthew Ginks,C. Aldo Rinaldi,Reza Razavi,Hervé Delingette,Nicholas Ayache +7 more
TL;DR: A velocity-based objective function can properly identify regional maximum contraction stresses, contraction rates, and relaxation rates simultaneously with intact model complexity, and the proposed framework is insensitive to initial parameters with the adopted derivative-free optimization algorithm.
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Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis.
Wen Wei,Emilie Poirion,Benedetta Bodini,Stanley Durrleman,Olivier Colliot,Bruno Stankoff,Nicholas Ayache +6 more
TL;DR: The use of the three-dimensional fully convolutional neural networks is proposed to predict FLAIR pulse sequences from other MRI pulse sequences and results show that this method is competitive for FLAIR synthesis.
Posted Content
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
TL;DR: In this article, a semi-supervised learning procedure was developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence.
Image-based modeling of tumor growth in patients with glioma.
TL;DR: A patient-specific model of tumor growth may provide new means for analyzing the acquired images and evaluating patient’s options, as all observations in these data sets arise from one underlying physiological process – the tumor-induced change of the tissue.