M
Michel Dojat
Researcher at University of Grenoble
Publications - 180
Citations - 7742
Michel Dojat is an academic researcher from University of Grenoble. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 34, co-authored 169 publications receiving 6386 citations. Previous affiliations of Michel Dojat include French Institute of Health and Medical Research & French Institute for Research in Computer Science and Automation.
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Journal ArticleDOI
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper 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.
Journal ArticleDOI
A multicenter randomized trial of computer-driven protocolized weaning from mechanical ventilation
Francçois Lellouche,Jordi Mancebo,Philippe Jolliet,Jean Roeseler,Frédérique Schortgen,Michel Dojat,Belen Cabello,Lila Bouadma,Pablo Rodriguez,Salvatore Maurizio Maggiore,Marc Reynaert,Stefan Mersmann,Laurent Brochard +12 more
TL;DR: The specific computer-driven system used in this study can reduce mechanical ventilation duration and ICU length of stay, as compared with a physician-controlled weaning process.
Journal ArticleDOI
fMRI retinotopic mapping--step by step.
Jan Warnking,Michel Dojat,Anne Guérin-Dugué,Chantal Delon-Martin,Serge Olympieff,Nathalie Richard,Alain Chéhikian,Cristoph Segebarth +7 more
TL;DR: Besides reusing methods proposed by other researchers in the field, original ones are introduced: improved stimuli for the mapping of polar angle retinotopy, a method of assigning volume-based functional data to the surface, and a way of weighting phase information optimally to account for the SNR obtained locally.
Journal ArticleDOI
Clinical Evaluation of a Computer-controlled Pressure Support Mode
TL;DR: Automatic PSV increased the time spent within desired ventilation parameter ranges and apparently reduced periods of excessive workload.
Journal ArticleDOI
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
Olivier Commowick,Audrey Istace,Michael Kain,Baptiste Laurent,Florent Leray,Mathieu Simon,Sorina Camarasu-Pop,Pascal Girard,Roxana Ameli,Jean-Christophe Ferré,Anne Kerbrat,Thomas Tourdias,Frederic Cervenansky,Tristan Glatard,Jeremy Beaumont,Senan Doyle,Florence Forbes,Jesse Knight,April Khademi,Amirreza Mahbod,Chunliang Wang,Richard McKinley,Franca Wagner,John Muschelli,Elizabeth M. Sweeney,Eloy Roura,Xavier Lladó,Michel M. dos Santos,Wellington Pinheiro dos Santos,Abel G. Silva-Filho,Xavier Tomas-Fernandez,Hélène Urien,Isabelle Bloch,Sergi Valverde,Mariano Cabezas,Francisco Javier Vera-Olmos,Norberto Malpica,Charles R.G. Guttmann,Sandra Vukusic,Gilles Edan,Michel Dojat,Martin Styner,Simon K. Warfield,François Cotton,Christian Barillot +44 more
TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.