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Grzegorz Chlebus
Researcher at Fraunhofer Society
Publications - 16
Citations - 878
Grzegorz Chlebus is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Segmentation & Convolutional neural network. The author has an hindex of 6, co-authored 13 publications receiving 459 citations. Previous affiliations of Grzegorz Chlebus include Wrocław University of Technology & Radboud University Nijmegen.
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The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic,Patrick Ferdinand Christ,Eugene Vorontsov,Grzegorz Chlebus,Hao Chen,Qi Dou,Chi-Wing Fu,Xiao Han,Pheng-Ann Heng,Jürgen Hesser,Samuel Kadoury,Tomasz Konopczynski,Miao Le,Chunming Li,Xiaomeng Li,Jana Lipkova,John Lowengrub,Hans Meine,Jan Hendrik Moltz,Chris Pal,Marie Piraud,Xiaojuan Qi,Jin Qi,Markus Rempfler,Karsten Roth,Andrea Schenk,Anjany Sekuboyina,Ping Zhou,Christian Hülsemeyer,Marcel Beetz,Florian Ettlinger,Felix Gruen,Georgios Kaissis,Fabian Lohöfer,Rickmer Braren,Julian Walter Holch,Felix Hofmann,Wieland H. Sommer,Volker Heinemann,Colin Jacobs,Gabriel Efrain Humpire Mamani,Bram van Ginneken,Gabriel Chartrand,An Tang,Michal Drozdzal,Avi Ben-Cohen,Eyal Klang,Marianne M. Amitai,Eli Konen,Hayit Greenspan,Johan Moreau,Alexandre Hostettler,Luc Soler,Refael Vivanti,Adi Szeskin,Naama Lev-Cohain,Jacob Sosna,Leo Joskowicz,Bjoern H. Menze +58 more
TL;DR: The set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference on Medical Image Computing Computer Assisted Intervention (MICCAI) 2017 are reported.
Journal ArticleDOI
Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
Grzegorz Chlebus,Andrea Schenk,Jan Hendrik Moltz,Bram van Ginneken,Bram van Ginneken,Horst K. Hahn,Horst K. Hahn,Hans Meine,Hans Meine +8 more
TL;DR: A fully automatic method for liver tumor segmentation in CT images based on a 2D fully convolutional neural network with an object-based postprocessing step with a significant reduction of false positive findings when compared with the raw neural network output.
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Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering
TL;DR: A fully automatic method employing convolutional neural networks based on the 2D U-net architecture and random forest classifier to solve the automatic liver lesion segmentation problem of the ISBI 2017 Liver Tumor Segmentation Challenge (LiTS).
Journal ArticleDOI
Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections.
Grzegorz Chlebus,Grzegorz Chlebus,Hans Meine,Hans Meine,Smita Thoduka,Nasreddin Abolmaali,Bram van Ginneken,Bram van Ginneken,Horst K. Hahn,Horst K. Hahn,Andrea Schenk +10 more
TL;DR: The quality of automatic liver segmentations is on par with those from manual routines, which could lead to a reduction of segmentation time and a more consistent liver volume estimation across different observers.
Journal ArticleDOI
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI.
Anneke Meyer,Grzegorz Chlebus,Grzegorz Chlebus,Marko Rak,Daniel Schindele,Martin Schostak,Bram van Ginneken,Bram van Ginneken,Andrea Schenk,Hans Meine,Hans Meine,Horst K. Hahn,Andreas Schreiber,Christian Hansen +13 more
TL;DR: In this article, an anisotropic 3D multi-stream CNN architecture was proposed to produce a high-resolution isotropic prostate segmentation, and two variants of the architecture were investigated on two (dualplane) and three (tripleplane) image orientations, respectively.