Accepted or to appear

  • Chlebus G, Humpire Mamani GE, Schenk A, van Ginneken B, Meine H. “Mimicking Radiologists to Improve the Robustness of Deep-learning Based Automatic Liver Segmentation”, RSNA Annual Meeting.


  • Chlebus G, Abolmaali N, Schenk A, Meine H. “Relevance analysis of MRI sequences for automatic liver tumor segmentation”, Medical Imaging with Deep Learning.

  • Chlebus G, Meine H, Thoduka S, Abolmaali N, van Ginneken B, Hahn HK, Schenk A. “Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections”, PLoS ONE.

  • Bilic P, Christ PF, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu CW, Han X, Heng PA, Hesser J, Kadoury S. “The Liver Tumor Segmentation Benchmark (LiTS)”, arXiv preprint arXiv:1901.04056.


  • Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H. “Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing”, Scientific Reports.

  • Meine H, Chlebus G, Ghafoorian M, Endo I, Schenk A. “Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT”, arXiv preprint arXiv:1810.04017.

  • Chlebus G, Meine H, Abolmaali N, Schenk A. “Automatic Liver and Tumor Segmentation in Late-Phase MRI Using Fully Convolutional Neural Networks”, Proceedings of the Annual Meeting of the German Society of Computer- and Robot-Assisted Surgery.

  • Schenk A, Chlebus G, Meine H, Thoduka S, Abolmaali N. “Deep Learning for Liver Segmentation and Volumetry in Late Phase MRI”, European Congress of Radiology.

  • Hermes L, Wenzel M, Schröder T, Zeile M, Chlebus G, Brüning R. “Zur automatisierten Detektion und Klassifikation von Leberläsionen im CT der Leber”, RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren.


  • Chlebus G, Meine H, Moltz JH, Schenk A. “Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering”, arXiv preprint arXiv:1706.00842.

  • Chlebus G, Meine H, Endo I, Schenk A. “Comparison of Deep Learning and Shape Modeling for Automatic CT-based Liver Segmentation”, 3rd Conference on Image-Guided Interventions.

  • Chlebus G, Schenk A, Thoduka S, Abolmaali N, Endo I, Meine H. “Comparison of Model Initialization Methods for Liver Segmentation using Statistical Shape Models”, International Journal of Computer Assisted Radiology and Surgery.

  • Traulsen N, Schilling P, Thoduka S, Abolmaali N, Chlebus G, Strehlow J, Schenk A. “SIRT activity and dose calculation using an optimized territorial model for the liver”, International Journal of Computer Assisted Radiology and Surgery.


  • Nijhuis R, Brachmann C, Kamp F, Landry G, Weiler F, Traulsen N, Chlebus G, Ganswindt U, Thieke C, Krass S, Belka C. “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).

  • Weiler F, Chlebus G, Brachmann C, Traulsen N, Waring A, Rieder C, Lassen-Schmidt B, Krass S, Hahn H. “A Modular Analysis Tool for Imaging-Based Clinical Research in Radiation Therapy”, International Journal of Radiation Oncology*Biology*Physics.


  • Brachmann C, Waring A, Chlebus G, Traulsen N, Krass S. “A Tool for an Interactive Summary of a Radiotherapy Treatment”, Proceedings of 4D Treatment Planning Workshop.

  • Weiler F, Chlebus G, Rieder C, Moltz J, Waring A, Brachmann C, Traulsen N, Corr D, Wirtz S, Krass S, Hahn HK. “Building Blocks for Clinical Research in Adaptive Radiotherapy”, Proceedings of the Annual Meeting of the German Society of Computer- and Robot-Assisted Surgery.

  • Weiler F, Brachmann C, Traulsen N, Nijhuis R, Chlebus G, Schenk M, Corr D, Wirtz S, Ganswindt U, Thieke C, Belka C, Hahn HK. “Fast automated non-linear contour propagation for adaptive head and neck radiotherapy”, MICCAI Workshop on Imaging and Computer Assistance in Radiation Therapy ICART.


  • Samei G, Chlebus G, Székely G, Tanner C. “Adaptive confidence regions of motion predictions from population exemplar models”, MICCAI Workshop on Computational and Clinical Challenges in Abdominal Imaging.