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Ezequiel Geremia
Researcher at French Institute for Research in Computer Science and Automation
Publications - 7
Citations - 4178
Ezequiel Geremia is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Random forest & Image segmentation. The author has an hindex of 5, co-authored 6 publications receiving 3147 citations. Previous affiliations of Ezequiel Geremia include Microsoft.
Papers
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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
Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images.
Ezequiel Geremia,Olivier Clatz,Bjoern H. Menze,Ender Konukoglu,Antonio Criminisi,Nicholas Ayache +5 more
TL;DR: In an a posteriori analysis, it is shown how selected features during classification can be ranked according to their discriminative power and reveal the most important ones.
Book ChapterDOI
Spatial decision forests for MS lesion segmentation in multi-channel MR images
Ezequiel Geremia,Bjoern H. Menze,Olivier Clatz,Ender Konukoglu,Antonio Criminisi,Nicholas Ayache +5 more
TL;DR: A new algorithm is presented for the automatic segmentation of Multiple Sclerosis lesions in 3D MR images that builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume.
Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images
TL;DR: In this paper, a fully automatic algorithm is presented for the automatic segmentation of gliomas in 3D MR images, which builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classi cation of the volume.
Book ChapterDOI
Layered spatio-temporal forests for left ventricle segmentation from 4d cardiac MRI data
TL;DR: A new method for fully automatic left ventricle segmentation from 4D cardiac MR datasets is presented using two layers of spatio-temporal decision forests with almost no assumptions on the data nor explicitly specifying the segmentation rules.