M
Miguel Luengo-Oroz
Researcher at United Nations
Publications - 80
Citations - 2435
Miguel Luengo-Oroz is an academic researcher from United Nations. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 18, co-authored 67 publications receiving 1866 citations. Previous affiliations of Miguel Luengo-Oroz include Technical University of Madrid & Mines ParisTech.
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Journal ArticleDOI
Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms
Michiel Schaap,Coert Metz,Theo van Walsum,Alina G. van der Giessen,Annick C. Weustink,Nico R. Mollet,Christian Bauer,Hrvoje Bogunovic,Carlos Castro,Xiang Deng,Engin Dikici,Thomas F. O'Donnell,Michel Frenay,Ola Friman,Marcela Hernández Hoyos,Pieter H. Kitslaar,Karl Krissian,Caroline Kühnel,Miguel Luengo-Oroz,Maciej Orkisz,Örjan Smedby,Martin Styner,Andrzej Szymczak,Huseyin Tek,Chunliang Wang,Simon K. Warfield,Sebastian Zambal,Yong Zhang,Gabriel P. Krestin,Wiro J. Niessen,Wiro J. Niessen +30 more
TL;DR: A standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms and a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available.
Journal ArticleDOI
Cell Lineage Reconstruction of Early Zebrafish Embryos Using Label-Free Nonlinear Microscopy
Nicolas Olivier,Miguel Luengo-Oroz,Louise Duloquin,Emmanuel Faure,Thierry Savy,Israel Veilleux,Xavier Solinas,Delphine Débarre,Paul Bourgine,Andres Santos,Nadine Peyriéras,Emmanuel Beaurepaire +11 more
TL;DR: A framework for imaging and reconstructing unstained whole zebrafish embryos for their first 10 cell division cycles is designed and measurements along the cell lineage are reported with micrometer spatial resolution and minute temporal accuracy.
Journal ArticleDOI
Mapping the landscape of artificial intelligence applications against COVID-19
Joseph Bullock,Alexandra Luccioni,Katherine Hoffmann Pham,Cynthia Sin Nga Lam,Miguel Luengo-Oroz +4 more
TL;DR: In this paper, the authors present an overview of recent studies using Machine Learning and Artificial Intelligence to tackle many aspects of the COVID-19 crisis and highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.
Journal ArticleDOI
On the privacy-conscientious use of mobile phone data
Yves-Alexandre de Montjoye,Yves-Alexandre de Montjoye,Sébastien Gambs,Vincent D. Blondel,Geoffrey Canright,Nicolas de Cordes,Sébastien Deletaille,Kenth Engø-Monsen,Manuel García-Herranz,Jake Kendall,Cameron F. Kerry,Gautier Krings,Emmanuel Letouzé,Miguel Luengo-Oroz,Nuria Oliver,Luc Rocher,Alex Rutherford,Zbigniew Smoreda,Jessica Steele,Erik Wetter,Erik Wetter,Alex Pentland,Linus Bengtsson +22 more
TL;DR: Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.
Journal ArticleDOI
Crowdsourcing Malaria Parasite Quantification: An Online Game for Analyzing Images of Infected Thick Blood Smears
TL;DR: The findings support the conclusion that nonexperts are able to rapidly learn how to identify the typical features of malaria parasites in digitized thick blood samples and that combining the analyses of several users provides similar parasite counting accuracy rates as those of expert microscopists.