• Chlebus G. “Deep learning-based segmentation in multimodal abdominal imaging”, PhD Dissertation, ISBN: 9788396448002.

  • Chlebus G, Schenk A, Hahn HK, van Ginneken B, Meine H. “Robust Segmentation Models using an Uncertainty Slice Sampling-Based Annotation Workflow”, IEEE Access.

  • Kock F, Thielke F, Chlebus G, Meine H. “Confidence Histograms for Model Reliability Analysis and Temperature Calibration”, Medical Imaging with Deep Learning.

  • Hänsch A, Chlebus G, Meine H, Thielke F, Kock F, Paulus T, Abolmaali N, Schenk A. “Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks”, Scientific Reports.


  • Tschigor C, Chlebus G, Schumann C. “Deep Learning-basierte Oberflächenrekonstruktion aus Binärmasken”, Bildverarbeitung für die Medizin 2021.


  • Meyer A, Chlebus G, Rak M, Schindele D, Schostak M, van Ginneken B, Schenk A, Meine H, Hahn HK, Schreiber A, Hansen Ch. “Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI”, Computer Methods and Programs in Biomedicine.

  • Schnurr AK, Schöben M, Hermann I, Schmidt R, Chlebus G, Schad LR, Gass A, Zöllner FG. “Relevance Analysis of MRI Sequences for MS Lesion Detection”, European Society of Magnetic Resonance in Medicine and Biology.

  • Göhler A, Hsu TMH, Lacson R, Gujrathi I, Hashemi R, Chlebus G, Szolovits P, Khorasani R. “Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs”, Journal of the American College of Radiology.

  • Göhler A, Chlebus G, et al. “Assessing liver tumor burden change on consecutive liver MRIs – an end-to-end deep learning application”, BIDMC’s Artificial Intelligence/Machine Learning Symposium.

  • Altun HC, Chlebus G, Jacobs C, Meine H, van Ginneken B, Hahn HK. “Feasibility of End-To-End Trainable Two-Stage U-Net for Detection of Axillary Lymph Nodes in Contrast-Enhanced CT Based Scans on Sparse Annotations”, SPIE Medical Imaging.


  • 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.