K
Koen Van Leemput
Researcher at Technical University of Denmark
Publications - 116
Citations - 11340
Koen Van Leemput is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 31, co-authored 104 publications receiving 8392 citations. Previous affiliations of Koen Van Leemput include Aalto University & Harvard University.
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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
The Multimodal Brain TumorImage Segmentation Benchmark (BRATS)
TL;DR: The set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences are reported, finding that different algorithms worked best for different sub-regions, but that no single algorithm ranked in the top for all sub-Regions simultaneously.
Journal ArticleDOI
A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI
Juan Eugenio Iglesias,Jean C. Augustinack,Khoa Nguyen,Christopher M. Player,Allison Player,Michelle F. Wright,Nicole Roy,Matthew P. Frosch,Ann C. McKee,Lawrence L. Wald,Bruce Fischl,Koen Van Leemput +11 more
TL;DR: The results show that the atlas and companion segmentation method can segment T1 and T2 images, as well as their combination, replicate findings on mild cognitive impairment based on high-resolution T2 data, and can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy.
Journal Article
Automated Segmentation of Hippocampal Subfields From Ultra-High Resolution In Vivo MRI
Koen Van Leemput,Koen Van Leemput,Akram Bakkour,Thomas Benner,Graham Wiggins,Lawrence L. Wald,Lawrence L. Wald,Jean C. Augustinack,Bradford C. Dickerson,Polina Golland,Bruce Fischl,Bruce Fischl +11 more
TL;DR: In this article, a method for segmenting the hippo-campal subfields in ultra-high resolution MRI data in a fully automated fashion is presented. But the method is limited to the hippocampal area.
Journal ArticleDOI
Automated segmentation of hippocampal subfields from ultra‐high resolution in vivo MRI
Koen Van Leemput,Koen Van Leemput,Akram Bakkour,Thomas Benner,Graham Wiggins,Lawrence L. Wald,Lawrence L. Wald,Jean C. Augustinack,Bradford C. Dickerson,Polina Golland,Bruce Fischl,Bruce Fischl +11 more
TL;DR: This article presents a computational method for segmenting the hippocampal subfields in ultra‐high resolution MRI data in a fully automated fashion using Bayesian inference, and shows that automated volume measurements of the larger subfields correlate well with manual volume estimates.