Grzegorz Chlebus

  • Research Scientist
  • Image Segmentation
  • Software Development



Bilic P, Christ PF, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu C-W, Han X, Heng P-A, Hesser J, Kadoury S, Konopczynski T, Le M, Li C, Li X, Lipkovà J, Lowengrub J, Meine H, Moltz JH, Pal C, Piraud M, Qi X, Qi J, Rempfler M, Roth K, Schenk A, Sekuboyina A, Vorontsov E, Zhou P, Hülsemeyer C, Beetz M, Ettlinger F, Gruen F, Kaissis G, Lohöfer F, Braren R, Holch J, Hofmann F, Sommer W, Heinemann V, Jacobs C, Mamani GEH, van Ginneken B, Chartrand G, Tang A, Drozdzal M, Ben-Cohen A, Klang E, Amitai MM, Konen E, Greenspan H, Moreau J, Hostettler A, Soler L, Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L, Menze BH (2019) The Liver Tumor Segmentation Benchmark (LiTS). arXiv:1901.04056
Chlebus G, Meine H, Thoduka S, Abolmaali N, van Ginneken B, Hahn HK, Schenk A (2019) Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections. PLoS ONE 14(5):e0217228
Chlebus G, Abolmaali N, Schenk A, Meine H (2019) Relevance analysis of MRI sequences for automatic liver tumor segmentation. Proceedings of Medical Imaging with Deep Learning (MIDL 2019). pp 1–4


Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H (2018) Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep 8:15497
Chlebus G, Meine H, Abolmaali N, Schenk A (2018) Automatic Liver and Tumor Segmentation in Late-Phase MRI Using Fully Convolutional Neural Networks. Proceedings of CURAC. pp 195–200
Schenk A, Chlebus G, Meine H, Thoduka S, Abolmaali N (2018) Deep learning for liver segmentation and volumetry in late phase MRI. Proceedings of European Congress on Radiology (ECR). Springer, S474


Chlebus G, Meine H, Moltz JH, Schenk A (2017) Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering. arXiv:1706.00842
Chlebus G, Schenk A, Thoduka S, Abolmaali N, Endo I, Meine H (2017) Comparison of Model Initialization Methods for Liver Segmentation using Statistical Shape Models. International Journal of Computer Assisted Radiology and Surgery. pp 215–216
Traulsen N, Schilling P, Thoduka S, Abolmaali N, Chlebus G, Strehlow J, Schenk A (2017) SIRT activity and dose calculation using an optimized territorial model for the liver. International Journal of Computer Assisted Radiology and Surgery. pp 177–178


Nijhuis R, Brachmann C, Kamp F, Landry G, Weiler F, Traulsen N, Chlebus G, Ganswindt U, Thieke C, Krass S, Belka C (2016) Validation of a novel contour mapping method to facilitate adaptive radiotherapy in head and neck cancer patients. Proceedings of 22. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO), Mannheim
Weiler F, Chlebus G, Brachmann C, Traulsen N, Waring A, Rieder C, Lassen-Schmidt B, Krass S, Hahn H (2016) A Modular Analysis Tool for Imaging-Based Clinical Research in Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics. pp E418–E419


Brachmann C, Waring A, Chlebus G, Traulsen N, Krass S (2015) A Tool for an Interactive Summary of a Radiotherapy Treatment. Proceedings of 4D Treatment Planning Workshop 2015
Weiler F, Chlebus G, Rieder C, Moltz J, Waring A, Brachmann C, Traulsen N, Corr D, Wirtz S, Krass S, Hahn HK (2015) Building Blocks for Clinical Research in Adaptive Radiotherapy. Proc. of CURAC 2015. pp 139–144
Weiler F, Brachmann C, Traulsen N, Nijhuis R, Chlebus G, Schenk M, Corr D, Wirtz S, Ganswindt U, Thieke C, Belka C, Hahn HK (2015) Fast automated non-linear contour propagation for adaptive head and neck radiotherapy. MICCAI Workshop on Imaging and Computer Assistance in Radiation Therapy ICART