S
Senan Doyle
Researcher at French Institute for Research in Computer Science and Automation
Publications - 26
Citations - 4057
Senan Doyle is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 7, co-authored 20 publications receiving 2939 citations.
Papers
More filters
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
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.
Posted ContentDOI
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.
Proceedings ArticleDOI
Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation
TL;DR: A technique for fusing the output of multiple Magnetic Resonance sequences to robustly and accurately segment brain lesions is proposed, based on a Bayesian multi-sequence Markov model that includes weight parameters to account for the relative importance and control the impact of each sequence.
Journal ArticleDOI
Spatial risk mapping for rare disease with hidden Markov fields and variational EM
TL;DR: In this paper, a nonstandard discrete hidden Markov model prior is designed to favor a smooth risk variation, and the model parameters are estimated using an EM algorithm and a mean field approximation for which they develop a new initialization strategy appropriate for spatial Poisson mixtures.