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@inproceedings{diebold-et-al-ssvm15,
 author = {J. Diebold and N. Demmel and C. Hazirbas and M. Möller and D. Cremers},
 title = {Interactive Multi-label Segmentation of RGB-D Images},
 booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)},
 year = {2015},
 month = {june},
 keywords = {diebold, segmentation},
 doi = {10.1007/978-3-319-18461-6_24},
}

@inproceedings{hazirbas-et-al-ssvm15,
 author = {C. Hazirbas and J. Diebold and D. Cremers},
 title = {Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation},
 booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)},
 year = {2015},
 month = {june},
 keywords = {diebold, segmentation},
 doi = {10.1007/978-3-319-18461-6_20},
 award = {Oral Presentation},
}

@inproceedings{rodola-3dimpvt11-2,
 author = {A. Albarelli and E. Rodola and A. Torsello},
 title = {A Non-Cooperative Game for 3D Object Recognition in Cluttered Scenes},
 booktitle = {International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)},
 year = {2011},
 pages = {252-259},
 doi = {10.1109/3DIMPVT.2011.39},
 titleurl = {rodola-3dimpvt11-2.pdf},
 keywords = {Shape Analysis, Recognition, Correspondence, Segmentation},
}

@incollection{Cremers-11,
 author = {D. Cremers},
 title = {Image Segmentation with Shape Priors: Explicit Versus Implicit Representations},
 booktitle = {Handbook of Mathematical Methods in Imaging},
 publisher = {Springer},
 year = {2011},
 pages = {1453-1487},
 titleurl = {cremers_handbook2011.pdf},
 keywords = {shape, segmentation},
 topic = {Shape},
}

@incollection{Klodt-et-al-13,
 author = {M. Klodt and F. Steinbruecker and D. Cremers},
 title = {Moment Constraints in Convex Optimization for Segmentation and Tracking},
 booktitle = {Advanced Topics in Computer Vision},
 publisher = {Springer},
 year = {2013},
 titleurl = {Klodt-et-al-13.pdf},
 keywords = {segmentation, convex-relaxation, medical imaging},
 topic = {Convex Relaxation Methods, Segmentation},
}

@inproceedings{BBW06,
 author = {T. Brox and A. Bruhn and J. Weickert},
 title = {Variational motion segmentation with level sets},
 booktitle = {European Conference on Computer Vision (ECCV)},
 year = {2006},
 editor = {A. Leonardis and H. Bischof and A. Pinz},
 volume = {3951},
 series = {LNCS},
 pages = {471--483},
 address = {Graz, Austria},
 month = {may},
 publisher = {Springer},
 copyright = {{<a href="http://www.springer.de/comp/lncs/index.html">Springer-Verlag
    Berlin Heidelberg 2006</a>}},
 titleurl = {brox_eccv06_of.pdf},
 topic = {Optic Flow, Segmentation, Level Sets, Motion},
 keywords = {optical-flow, segmentation},
}

@article{Schoenemann-et-al-ijcv12,
 author = {T. Schoenemann and F. Kahl and S. Masnou and D. Cremers},
 title = {A linear framework for region-based image segmentation and inpainting involving curvature penalization},
 journal = {International Journal of Computer Vision},
 year = {2012},
 month = {aug},
 volume = {99},
 issue = {1},
 pages = {53--68},
 titleurl = {schoenemann_et_al_ijcv12.pdf},
 topic = {Segmentation},
 keywords = {segmentation, curvature},
}

@inproceedings{Cremers-Grady-06,
 author = {D. Cremers and L. Grady},
 title = {Statistical priors for combinatorial optimization:
                  efficient solutions via {G}raph {C}uts},
 booktitle = {European Conference on Computer Vision (ECCV)},
 year = {2006},
 editor = {A. Leonardis and H. Bischof and A. Pinz},
 volume = {3953},
 series = {LNCS},
 pages = {263--274},
 address = {Graz, Austria},
 month = {may},
 publisher = {Springer},
 copyright = {{<a href="http://www.springer.de/comp/lncs/index.html">Springer-Verlag
    Berlin Heidelberg 2006</a>}},
 keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d
    ct prostate},
 titleurl = {cremers_grady_eccv06.pdf},
 topic = {Statistics, Texture},
}

@inproceedings{Cremers-Schnoerr-02b,
 author = {D. Cremers and C. Schnörr},
 title = {Statistical shape knowledge in variational motion segmentation},
 booktitle = {1st {I}nternat. {W}orkshop on {G}enerative-{M}odel-{B}ased {V}ision},
 year = {2002},
 editor = {A. Pece and Y. N. Wu and R. Larsen},
 address = {Copenhagen},
 month = {June, 2},
 publisher = {Univ. of Copenhagen},
 titleurl = {cremers_gmbv02.ps.gz},
 topic = {Shape, Optic Flow, Statistics, Segmentation, Motion},
 keywords = {optical-flow, segmentation},
}

@article{Cremers-12,
 author = {D. Cremers},
 title = {Optimal Solutions for Semantic Image Decomposition},
 journal = {Image and Vision Computing},
 year = {2012},
 volume = {30},
 pages = {476--477},
 number = {8},
 titleurl = {cremers_ivc12.pdf},
 keywords = {convex-relaxation, segmentation},
}

@inproceedings{Manay-et-al-05,
 author = {S. Manay and D. Cremers and A. J. Yezzi and S. Soatto},
 title = {One-shot integral invariant shape priors for variational segmentation},
 booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)},
 volume = {3757},
 editor = {A. Rangarajan and B. Vemuri and A. L. Yuille},
 series = {LNCS},
 year = {2005},
 pages = {414--426},
 keywords = {image-segmentation,shape,Dijkstra + PDE},
 topic = {Segmentation, Shape, Correspondence},
}

@inproceedings{Rousson-Cremers-05,
 author = {M. Rousson and D. Cremers},
 title = {Efficient kernel density estimation of shape and intensity priors
    for level set segmentation},
 booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
 year = {2005},
 volume = {1},
 pages = {757--764},
 keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d ct prostate, medical imaging},
 titleurl = {rousson_cremers05.pdf},
 topic = {Shape, Segmentation, Statistics, Level Sets},
}

@inproceedings{Fluck-et-al-06,
 author = {O. Fluck and S. Aharon and D. Cremers and M. Rousson},
 title = {GPU histogram computation},
 booktitle = {ACM SIGGRAPH posters and demos},
 year = {2006},
 keywords = {image segmentation, Parzen, Level Sets},
 titleurl = {fluck_et_al_siggraph06_abstract.pdf},
 topic = {Segmentation, Statistics, Level Sets},
}

@inproceedings{Cremers-et-al-07,
 author = {D. Cremers and O. Fluck and M. Rousson and S. Aharon},
 title = {A probabilistic level set formulation for interactive organ
                  segmentation},
 booktitle = {Proc. of the SPIE Medical Imaging},
 year = {2007},
 month = {feb},
 address = {San Diego, USA},
 editors = {E. Krupinski and A. Amini and M. Sonka},
 keywords = {image segmentation, Parzen, Level Sets, medical imaging},
 titleurl = {cremers_et_al_spie07.pdf},
 topic = {Segmentation, Statistics, Level Sets},
}

@inproceedings{Kohlberger-et-al-06,
 author = {T. Kohlberger and D. Cremers and M. Rousson and R. Ramaraj},
 title = {4D shape priors for level set segmentation of the left
                  myocardium in {SPECT} sequences},
 booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
 volume = {4190},
 series = {LNCS},
 pages = {92--100},
 year = {2006},
 month = {oct},
 keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d
    ct prostate, medical imaging},
 titleurl = {kohlberger_et_al_miccai06.pdf},
 topic = {Shape, Segmentation, Medical Image Analysis, Level Sets},
}

@inproceedings{souiai-et-al-wiccv13,
 author = {M. Souiai and C. Nieuwenhuis and E. Strekalovskiy and D. Cremers},
 title = {Convex Optimization for Scene Understanding},
 booktitle = {ICCV Workshop on Graphical Models for Scene Understanding},
 year = {2013},
 titleurl = {souiai-et-al-wiccv13.pdf},
 keywords = {segmentation},
}

@inproceedings{bergbauer-et-al-wiccv13,
 author = {J. Bergbauer and C. Nieuwenhuis and M. Souiai and D. Cremers},
 title = {Proximity Priors for Variational Semantic Segmentation and Recognition},
 booktitle = {ICCV Workshop on Graphical Models for Scene Understanding},
 year = {2013},
 titleurl = {bergbauer-et-al-wiccv13.pdf},
 doi = {10.1109/ICCVW.2013.132},
 keywords = {segmentation,diebold},
}

@inproceedings{nieuwenhuis-et-al-dagm10b,
 author = {C. Nieuwenhuis and B. Berkels and M. Rumpf},
 title = {Interactive Motion Segmentation},
 booktitle = {Pattern Recognition (Proc. DAGM)},
 address = {Heidelberg, Germany},
 series = {LNCS},
 publisher = {Springer},
 month = {sep},
 year = {2010},
 volume = {6376},
 pages = {483--492},
 titleurl = {nieuwenhuis_et_al_dagm10b.pdf},
 keywords = {segmentation,optical-flow},
}

@inproceedings{Nieuwenhuis_emmcvpr11,
 author = {C. Nieuwenhuis and E. Toeppe and D. Cremers},
 title = {Space-Varying Color Distributions for Interactive Multiregion Segmentation: Discrete versus Continuous Approaches},
 year = {2011},
 booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)},
 topic = {segmentation, Convex Relaxation Methods},
 titleurl = {nieuwenhuis_et_al_emmcvpr11.pdf},
 pages = {177-190},
 keywords = {segmentation, convex-relaxation},
}

@inproceedings{KC11:iccv,
 author = {M. Klodt and D. Cremers},
 title = {A Convex Framework for Image Segmentation with Moment Constraints},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 year = {2011},
 titleurl = {kc11_iccv.pdf},
 topic = {segmentation, Convex Relaxation Methods},
 keywords = {segmentation, convex-relaxation, medical imaging},
}

@inproceedings{Strekalovskiy-et-al-eccv12,
 author = {E. Strekalovskiy and C. Nieuwenhuis and D. Cremers},
 title = {Nonmetric Priors for Continuous Multilabel Optimization},
 booktitle = {European Conference on Computer Vision (ECCV)},
 year = {2012},
 address = {Firenze, Italy},
 month = {oct},
 publisher = {Springer},
 topic = {Convex Relaxation Methods,Segmentation},
 keywords = {convex-relaxation, Segmentation},
}

@article{nieuwenhuis-cremers-pami12_2,
 author = {C. Nieuwenhuis and D. Cremers},
 title = {Spatially Varying Color Distributions for Interactive Multi-Label Segmentation},
 journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
 year = {2013},
 volume = {35},
 number = {5},
 pages = {1234-1247},
 titleurl = {nieuwenhuis-cremers-pami12_2.pdf},
 topic = {Segmentation},
 keywords = {convex-relaxation, segmentation, medical imaging},
}

@article{nieuwenhuis-et-al-ijcv13,
 author = {C. Nieuwenhuis and E. Toeppe and D. Cremers},
 title = {A Survey and Comparison of Discrete and Continuous Multi-label Optimization Approaches for the Potts Model},
 journal = {International Journal of Computer Vision},
 volume = {104},
 number = {3},
 pages = {223-240},
 year = {2013},
 month = {sep},
 keywords = {convex-relaxation, Segmentation},
}

@inproceedings{ufer_et_al_eccv12,
 author = {N. Ufer and M. Souiai and D. Cremers},
 title = {Wehrli 2.0: An Algorithm for ”Tidying up Art”},
 booktitle = {VISART “Where Computer Vision Meets Art” workshop, ECCV 2012},
 year = {2012},
 address = {Firenze, Italy},
 month = {oct},
 publisher = {Springer},
 topic = {Segmentation},
 keywords = {convex-relaxation, segmentation},
 titleurl = {ufer_et_al_eccv12.pdf},
}

@inproceedings{toeppe_et_al_cvpr13,
 author = {E. Toeppe and C. Nieuwenhuis and D. Cremers},
 title = {Volume Constraints for Single View Reconstruction},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2013},
 address = {Portland, USA},
 topic = {Segmentation},
 keywords = {singleview, convex-relaxation,segmentation},
 titleurl = {toeppe_et_al_cvpr13.pdf},
}

@inproceedings{lellmann-et-al-iccv2013,
 author = {J. Lellmann and E. Strekalovskiy and S. Koetter and D. Cremers},
 title = {Total Variation Regularization for Functions with Values in a Manifold},
 year = {2013},
 address = {Sydney, Australia},
 month = {December},
 titleurl = {lellmann-et-al-iccv2013.pdf},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 topic = {Convex Relaxation Methods},
 keywords = {convex-relaxation, Segmentation},
}

@inproceedings{Nieuwenhuis-et-al-iccv13,
 author = {C. Nieuwenhuis and E. Strekalovskiy and D. Cremers},
 title = {Proportion Priors for Image Sequence Segmentation},
 year = {2013},
 address = {Sydney, Australia},
 month = {December},
 titleurl = {nieuwenhuis-et-al-iccv2013.pdf},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 topic = {Convex Relaxation Methods},
 keywords = {convex-relaxation, Segmentation},
}

@inproceedings{stuehmer-et-al-iccv2013,
 author = {J. Stühmer and P. Schröder and D. Cremers},
 title = {Tree Shape Priors with Connectivity Constraints using Convex Relaxation on General Graphs},
 year = {2013},
 address = {Sydney, Australia},
 month = {December},
 titleurl = {stuehmer-et-al-iccv2013.pdf},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 topic = {Segmentation, Shape Priors},
 keywords = {Convex-Relaxation, Segmentation, shape-priors, medical imaging},
 award = {Oral Presentation},
}

@inproceedings{Strobel-et-al-gcpr2014,
 author = {M. Strobel and J. Diebold and D. Cremers},
 title = {Flow and Color Inpainting for Video Completion},
 booktitle = {German Conference on Pattern Recognition (GCPR)},
 year = {2014},
 address = {M\"unster, Germany},
 month = {September},
 keywords = {video completion, video inpainting, disocclusion, temporal consistency, segmentation, optical flow,diebold},
 doi = {10.1007/978-3-319-11752-2_23},
 award = {Oral Presentation},
}

@inproceedings{Nieuwenhuis-et-al-eccv14,
 author = {C. Nieuwenhuis and S. Hawe and M. Kleinsteuber and D. Cremers},
 title = {Co-Sparse Textural Similarity for Interactive Segmentation},
 booktitle = {European Conference on Computer Vision (ECCV)},
 year = {2014},
 titleurl = {nieuwenhuis-et-al-eccv14.pdf},
 keywords = {convex-relaxation, Segmentation},
}

@mastersthesis{hazirbas2014msc,
 author = {C Hazirbas},
 title = {Feature Selection and Learning for Semantic Segmentation},
 school = {Technical University Munich},
 address = {Germany},
 year = {2014},
 month = {June},
 keywords = {feature selection, semantic segmentation, variational image segmentation, student-project},
}

@inproceedings{Stuehmer-Cremers-emmcvpr15,
 author = {J. Stühmer and D. Cremers},
 title = {A Fast Projection Method for Connectivity Constraints in Image Segmentation},
 booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)},
 editor = {X.-C. Tai and E. Bae and T. F. Chan and M. Lysaker},
 series = {LNCS},
 year = {2015},
 keywords = {image-segmentation,constrained convex optimization,shape-priors,medical imaging},
 topic = {Segmentation, Shape Priors},
}

@inproceedings{Gomez-et-al-ismrm15,
 author = {P.A. Gómez and T. Sprenger and A.A. López and J.I. Sperl and B. Fernandez and M. Molina-Romero and X. Liu and V. Golkov and M. Czisch and P. Saemann and M.I. Menzel and B.H. Menze},
 title = {Using Diffusion and Structural {MRI} for the Automated Segmentation of Multiple Sclerosis Lesions},
 booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting},
 year = {2015},
 keywords = {magnetic resonance imaging, diffusion MRI, segmentation, medical imaging},
}

@inproceedings{Golkov-et-al-miccai2015-qDL,
 author = {V. Golkov and A. Dosovitskiy and P. Sämann and J. I. Sperl and T. Sprenger and M. Czisch and M. I. Menzel and P. A. Gómez and A. Haase and T. Brox and D. Cremers},
 title = {{q-Space} Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion {MRI} Scans},
 booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
 month = {oct},
 year = {2015},
 address = {Munich, Germany},
 keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, medical imaging, deep learning},
}

@inproceedings{jaimez15_mocoop,
 author = {M. Jaimez and M. Souiai and J. Stueckler and J. Gonzalez-Jimenez and D. Cremers},
 title = {Motion Cooperation: Smooth Piece-Wise Rigid Scene Flow from RGB-D Images},
 booktitle = {Proc. of the Int. Conference on 3D Vision (3DV)},
 month = {oct},
 year = {2015},
 keywords = {scene-flow,motion segmentation,rgb-d,primal-dual},
 titleurl = {jaimez_et_al_3dv15.pdf},
}

@inproceedings{souiai-iccv15,
 author = {M. Souiai and M. R. Oswald and Y. Kee and J. Kim and M. Pollefeys and D. Cremers},
 title = {Entropy Minimization for Convex Relaxation Approaches},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 year = {2015},
 address = {Santiago, Chile},
 titleurl = {souiai-iccv15.pdf},
 keywords = {convex relaxation,DC programming, image segmentation, 3D reconstruction},
}

@inproceedings{Delong-et-al-emmcvpr11,
 author = {A. Delong and L. Gorelick and F. R. Schmidt and O. Veksler and Y. Boykov},
 title = {Interactive Segmentation with Super-Labels},
 booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)},
 series = {LNCS},
 volume = {6819},
 pages = {147--162},
 month = {Jul},
 year = {2011},
 publisher = {Springer},
 address = {Saint Petersburg, Russia},
 keywords = {segmentation},
}

@inproceedings{Schmidt-Boykov-eccv12,
 author = {F. R. Schmidt and Y. Boykov},
 title = {Hausdorff Distance Constraint for Multi-Surface Segmentation},
 booktitle = {European Conference on Computer Vision (ECCV)},
 series = {LNCS},
 volume = {7572},
 pages = {598--611},
 month = {Oct},
 year = {2012},
 publisher = {Springer},
 address = {Florence, Italy},
 keywords = {segmentation},
}

@inproceedings{Gorelick-et-al-eccv12,
 author = {L. Gorelick and F. R. Schmidt and Y. Boykov and A. Delong and A. Ward},
 title = {Segmentation with non-linear regional constraints via line-search cuts},
 booktitle = {European Conference on Computer Vision (ECCV)},
 series = {LNCS},
 volume = {7572},
 pages = {583--597},
 month = {Oct},
 year = {2012},
 publisher = {Springer},
 address = {Florence, Italy},
 keywords = {segmentation},
}

@inproceedings{Gorelick-et-al-cvpr13,
 author = {L. Gorelick and F. R. Schmidt and Y. Boykov},
 title = {Fast Trust Region for Segmentation},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 month = {Jun},
 year = {2013},
 address = {Portland, Oregon},
 keywords = {segmentation},
}

@inproceedings{Nagaraja-et-al-iccv15,
 author = {N. Nagaraja and F. R. Schmidt and T. Brox},
 title = {Video Segmentation with Just a Few Strokes},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 month = {Dec},
 year = {2015},
 address = {Santiago, Chile},
 keywords = {segmentation, convex-relaxation},
}

@inproceedings{Golkov-et-al-isbi2016,
 author = {V. Golkov and T. Sprenger and J. I. Sperl and M. I. Menzel and M. Czisch and P. Sämann and D. Cremers},
 title = {Model-Free Novelty-Based Diffusion {MRI}},
 booktitle = {{IEEE} International Symposium on Biomedical Imaging ({ISBI})},
 month = {apr},
 year = {2016},
 address = {Prague, Czech Republic},
 keywords = {magnetic resonance imaging, diffusion MRI, novelty detection, q-space, machine learning, model-free diffusion MRI, segmentation, medical imaging},
}

@article{Golkov-et-al-tmi2016,
 author = {V. Golkov and A. Dosovitskiy and J. I. Sperl and M. I. Menzel and M. Czisch and P. Sämann and T. Brox and D. Cremers},
 title = {{q-Space} Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion {MRI} Scans},
 year = {2016},
 journal = {IEEE Transactions on Medical Imaging},
 volume = {35},
 issue = {5},
 keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, medical imaging, deep learning},
 issuetitle = {Special Issue on Deep Learning},
 award = {Special Issue on Deep Learning},
}

@inproceedings{lingni17iros,
 author = {L. Ma and J. Stueckler and C. Kerl and D. Cremers},
 title = {Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras},
 booktitle = {International Conference on Intelligent Robots and Systems (IROS)},
 address = {Vancouver, Canada},
 year = {2017},
 month = {Sep},
 keywords = {CNN, semantic segmentation, multi-view},
}

@inproceedings{lingni16icra,
 author = {L. Ma and C. Kerl and J. Stueckler and D. Cremers},
 title = {CPA-SLAM: Consistent Plane-Model Alignment for Direct RGB-D SLAM},
 booktitle = {International Conference on Robotics and Automation (ICRA)},
 year = {2016},
 month = {May},
 keywords = {RGB-D SLAM, semantic segmentation, vslam},
}

@inproceedings{hazirbasma2016fusenet,
 author = {C. Hazirbas and L. Ma and C. Domokos and D. Cremers},
 title = {{<a href="https://hazirbas.github.io/projects/fusenet/" target="_blank">FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture</a>}},
 booktitle = {Asian Conference on Computer Vision},
 year = {2016},
 month = {november},
 keywords = {segmentation, deep learning},
}

@inproceedings{golyanik2017multiframe,
 title = {Multiframe Scene Flow with Piecewise Rigid Motion},
 author = {V. Golyanik and K. Kim and R. Maier and M. Niessner and D. Stricker and J. Kautz},
 booktitle = {International Conference on 3D Vision (3DV)},
 year = {2017},
 month = {October},
 address = {Qingdao, China},
 award = {Spotlight Presentation},
 keywords = {scene-flow,motion segmentation,rgb-d},
}

@inproceedings{Golkov-et-al-ismrm2018-novelty,
 author = {V. Golkov and A. Vasilev and F. Pasa and I. Lipp and W. Boubaker and E. Sgarlata and F. Pfeiffer and V. Tomassini and D. K. Jones and D. Cremers},
 title = {{q-Space} Novelty Detection in Short Diffusion {MRI} Scans of Multiple Sclerosis},
 year = {2018},
 booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting},
 keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation},
}

@article{mueller2021,
 title = {Rotation-Equivariant Deep Learning for Diffusion {MRI}},
 author = {P. Müller and V. Golkov and V. Tomassini and D. Cremers},
 year = {2021},
 journal = {arXiv preprint},
 eprint = {2102.06942},
 eprinttype = {arXiv},
 primaryclass = {cs.CV},
 keywords = {deep learning, diffusion MRI, equivariant deep learning, rotation-equivariance, magnetic resonance imaging, multiple sclerosis, image segmentation, medical imaging},
}

@inproceedings{Mueller2021-ISMRM,
 title = {Rotation-Equivariant Deep Learning for Diffusion {MRI} (short version)},
 author = {P. Müller and V. Golkov and V. Tomassini and D. Cremers},
 year = {2021},
 booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting},
 keywords = {deep learning, diffusion MRI, equivariant deep learning, rotation-equivariance, magnetic resonance imaging, multiple sclerosis, image segmentation, medical imaging},
}

@article{Wimmer2023,
 title = {Scale-Equivariant Deep Learning for 3D Data},
 author = {T Wimmer and V Golkov and HN Dang and M Zaiss and A Maier and D Cremers},
 year = {2023},
 journal = {arXiv preprint},
 eprint = {2304.05864},
 eprinttype = {arXiv},
 primaryclass = {cs.CV},
 keywords = {deep learning, equivariant deep learning, scale-equivariance, magnetic resonance imaging, image segmentation, medical imaging},
}

@inproceedings{weber2024flattening,
 title = {Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball},
 author = {S Weber and B Zöngür and N Araslanov and D Cremers},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 keywords = {segmentation, hyperbolic, calibration},
 titleurl = {weber_hyperbolic.png},
 year = {2024},
 eprint = {2404.03778},
 eprinttype = {arXiv},
 eprintclass = {cs.CV},
}