Fraunhofer Institute for Digital Medicine MEVISGrzegorz Chlebus
Publications
2023
Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, Szeskin A, Jacobs C, Mamani GEH, Chartrand G, Lohöfer F, Holch JW, Sommer W, Hofmann F, Hostettler A, Lev-Cohain N, Drozdzal M, Amitai MM, Vivanti R, Sosna J, Ezhov I, Sekuboyina A, Navarro F, Kofler F, Paetzold JC, Shit S, Hu X, Lipková J, Rempfler M, Piraud M, Kirschke J, Wiestler B, Zhang Z, Hülsemeyer C, Beetz M, Ettlinger F, Antonelli M, Bae W, Bellver M, Bi L, Chen H, Chlebus G, Dam EB, Dou Q, Fu C-W, Georgescu B, Giró-i-Nieto X, Gruen F, Han X, Heng P-A, Hesser J, Moltz JH, Igel C, Isensee F, Jäger P, Jia F, Kaluva KC, Khened M, Kim I, Kim J-H, Kim S, Kohl S, Konopczynski T, Kori A, Krishnamurthi G, Li F, Li H, Li J, Li X, Lowengrub J, Ma J, Maier-Hein K, Maninis K-K, Meine H, Merhof D, Pai A, Perslev M, Petersen J, Pont-Tuset J, Qi J, Qi X, Rippel O, Roth K, Sarasua I, Schenk A, Shen Z, Torres J, Wachinger C, Wang C, Weninger L, Wu J, Xu D, Yang X, Yu SC-H, Yuan Y, Yue M, Zhang L, Cardoso J, Bakas S, Braren R, Heinemann V, Pal C, Tang A, Kadoury S, Soler L, van Ginneken B, Greenspan H, Joskowicz L, Menze B (2023) The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis 84:102680
2022
Chlebus G (2022) Deep Learning-Based Segmentation in Multimodal Abdominal Imaging. Ph.D. thesis
Chlebus G, Schenk A, Hahn HK, Van Ginneken B, Meine H (2022) Robust Segmentation Models Using an Uncertainty Slice Sampling-Based Annotation Workflow. IEEE Access 10:4728–4738
Hänsch A, Chlebus G, Meine H, Thielke F, Kock F, Paulus T, Abolmaali N, Schenk A (2022) Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks. Sci Rep 12:12262
Kock F, Chlebus G, Thielke F, Schenk A, Meine H (2022) Hepatic artery segmentation with 3D convolutional neural networks. Proc. SPIE Medical Imaging 2022: Computer-Aided Diagnosis. Proc. SPIE 12033, pp 437–441
2021
Meyer A, Chlebus G, Rak M, Schindele D, Schostak M, van Ginneken B, Schenk A, Meine H, Hahn HK, Schreiber A, Hansen C (2021) Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI. Comput Methods Programs Biomed 200:105821
Tschigor C, Chlebus G, Schumann C (2021) Deep Learning-basierte Oberflächenrekonstruktion aus Binärmasken. Bildverarbeitung für die Medizin. Informatik aktuell, pp 304–309
2020
Goehler A, Harry Hsu T-M, Lacson R, Gujrathi I, Hashemi R, Chlebus G, Szolovits P, Khorasani R (2020) Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs. JACR 17(11):1475–1484
2019
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
2018
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
2017
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. Proceedings of the 31st International Congress and Exhibition of Computer Assisted Radiology and Surgery (CARS). 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. Proceedings of the 31st International Congress and Exhibition of Computer Assisted Radiology and Surgery (CARS). pp 177–178
2016
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
2015
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