@phdthesis{2020-PhD-Lotz,
  Author = {Johannes Lotz},
  Title = {Combined Local and Global Image Registration and its Application to Large-Scale Images in Digital Pathology},
  School = {Institute of Mathematics and Image Computing, University of Lübeck},
  Year = {2020},
}

@inproceedings{kuckertz2020SPIE,
  title={Deep learning based CT-CBCT image registration for adaptive radio therapy},
  author={Kuckertz, Sven and Papenberg, Nils and Honegger, Jonas and Morgas, Tomasz and Haas, Benjamin and Heldmann, Stefan},
  booktitle={Medical Imaging 2020: Image Processing},
  volume={11313},
  pages={113130Q},
  year={2020},
  organization={International Society for Optics and Photonics},
  url={http://dx.doi.org/10.1117%2F12.2549531}
}

@inproceedings{lange2020multilevel,
  title={Multilevel 2D-3D Intensity-Based Image Registration},
  author={Lange, Annkristin and Heldmann, Stefan},
  booktitle={International Workshop on Biomedical Image Registration},
  pages={57--66},
  year={2020},
  organization={Springer},
  keywords={NavEVAR},
  url={https://doi.org/10.1007/978-3-030-50120-4_6}
}

@InProceedings{kuckertz2020BVM,
author={Kuckertz, Sven and Papenberg, Nils and Honegger, Jonas and Morgas, Tomasz and Haas, Benjamin and Heldmann, Stefan},
editor={Tolxdorff, Thomas and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph},
title={Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy},
booktitle={Bildverarbeitung f{\"u}r die Medizin 2020},
year={2020},
publisher={Springer},
pages={229--229},
isbn={978-3-658-29267-6},
url={https://doi.org/10.1007/978-3-658-29267-6_51}
}

@inproceedings{kuckertz2020WBIR,
  title={Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapy},
  author={Kuckertz, Sven and Papenberg, Nils and Honegger, Jonas and Morgas, Tomasz and Haas, Benjamin and Heldmann, Stefan},
  booktitle={International Workshop on Biomedical Image Registration},
  pages={44--53},
  year={2020},
  organization={Springer},
  url={http://dx.doi.org/10.1007%2F978-3-030-50120-4_5}
}

@inproceedings{Lotz2020ISBI,
  author={Mercan, C. and Mooij, G.C.A.M. and Tellez, D. and Lotz, J. and Weiss, N. and van Gerven, M. and Ciompi, F.},
  title={Virtual Staining for Mitosis Detection in Breast Histopathology},
  journal={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  publisher={IEEE}
  year={2020},
  url={https://doi.org/10.1109/isbi45749.2020.9098409},
}

@article{garcia2020catheter,
  title={Catheter pose-dependent virtual angioscopy images for endovascular aortic repair: validation with a video graphics array (VGA) camera},
  author={García-Vázquez, Verónica and Matysiak, Florian and Jäckle, Sonja and Eixmann, Tim and Sieren, Malte Maria and von Haxthausen, Felix and Ernst, Floris},
  journal={Current Directions in Biomedical Engineering},
  volume={6},
  number={1},
  year={2020},
  keywords={NavEVAR},
  publisher={De Gruyter}
}

@inproceedings{Aggrawal2020WBIR,
author={Aggrawal, Hari Om and Andersen, Martin S. and Modersitzki, Jan},
title={An Image Registration Framework for Discontinuous Mappings Along Cracks},
booktitle={International Workshop on Biomedical Image Registration},
year={2020},
publisher={Springer},
pages={163--173},
url={{{https://doi.org/10.1007/978-3-030-50120-4_16}}},
doi={{{10.1007/978-3-030-50120-4_16}}}
}

@inproceedings{Aggrawal2020ISBI,
author={Aggrawal, Hari Om and Modersitzki, Jan},
title={Accelerating the Registration of Image Sequences by Spatio-Temporal Multilevel Strategies},
booktitle={Proceedings - International Symposium on Biomedical Imaging},
year={2020},
publisher={IEEE Computer Society},
pages={683--686},
url={{{https://doi.org/10.1109/ISBI45749.2020.9098520}}},
doi={{{10.1109/ISBI45749.2020.9098520}}}
}

@Article{ParisottoLellmannMasnouSchoenlieb2020,
  author  = {Parisotto, S. and Lellmann, J. and Masnou, S. and Sch{\"o}nlieb, C.},
  title   = {Higher-Order Total Directional Variation: Imaging Applications},
  journal = {{SIAM} Journal on Imaging Sciences},
  year    = {2020},
  volume  = {13},
  number  = {4},
  pages   = {2063--2104},
}

@inproceedings{hager2020variable,
  title={Variable fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg challenge},
  author={H{\"a}ger, Stephanie and Heldmann, Stefan and Hering, Alessa and Kuckertz, Sven and Lange, Annkristin},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={74--79},
  year={2020},
  organization={Springer}
}

@article{sieren2020rapid,
  title={Rapid study assessment in follow-up whole-body computed tomography in patients with multiple myeloma using a dedicated bone subtraction software},
  author={Sieren, MM and Brenne, F and Hering, A and Kienapfel, H and Gebauer, N and Oechtering, TH and F{\"u}rschke, A and Wegner, F and Stahlberg, E and Heldmann, S and others},
  journal={European Radiology},
  volume={30},
  number={6},
  pages={3198--3209},
  year={2020},
  publisher={Springer}
}

@article{lassen2020automatic,
  title={Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function},
  author={Lassen-Schmidt, Bianca and Hering, Alessa and Krass, Stefan and Meine, Hans},
  journal={arXiv preprint arXiv:2006.00083},
  year={2020}
}

@article{vogt2020connection,
  title={On the connection between dynamical optimal transport and functional lifting},
  author={Vogt, Thomas and Haase, Roland and Bednarski, Danielle and Lellmann, Jan},
  journal={arXiv preprint arXiv:2007.02587},
  year={2020}
}

@incollection{polzin2020discretize,
  title={A discretize--optimize approach for LDDMM registration},
  author={Polzin, Thomas and Niethammer, Marc and Vialard, Fran{\c{c}}ois-Xavier and Modersitzki, Jan},
  booktitle={Riemannian Geometric Statistics in Medical Image Analysis},
  pages={479--532},
  year={2020},
  publisher={Elsevier}
}

@article{jaeckle2019FOSS,
  author={J{\"a}ckle, Sonja and Eixmann, Tim and Schulz-Hildebrandt, Hinnerk and H{\"u}ttmann, Gereon and P{\"a}tz, Torben},
  title={Fiber optical shape sensing of flexible instruments for endovascular navigation},
  journal={International Journal of Computer Assisted Radiology and Surgery},
  year={2019},
  volume={14},
  number={12},
  pages={2137--2145},
  keywords = {NavEVAR},
  url={https://doi.org/10.1007/s11548-019-02059-0},
}

@inproceedings{VogtLellmann2019,
author={Vogt, Thomas and Lellmann, Jan},
title={Functional Liftings of Vectorial Variational Problems with Laplacian Regularization},
booktitle={Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Hofgeismar, Germany, June 30-July 4, 2019, Proceedings},
year={2019},
editor={Burger, Martin and Lellmann, Jan and Modersitzki, Jan},
publisher={Springer International Publishing},
pages={559--571},
url={{{https://arxiv.org/pdf/1904.00898.pdf}}},
doi={{{10.1007/978-3-030-22368-7_44}}}
}

@inproceedings{Brehmer2019,
author={Brehmer, Kai and Aggrawal, Hari Om and Heldmann, Stefan and Modersitzki, Jan},
title={Variational registration of multiple images with the SVD based SqN distance measure},
booktitle={Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Hofgeismar, Germany, June 30-July 4, 2019, Proceedings},
year={2019},
editor={Burger, Martin and Lellmann, Jan and Modersitzki, Jan},
publisher={Springer International Publishing},
pages={251--262},
url={{{https://arxiv.org/abs/1907.09732}}},
doi={{{10.1007/978-3-030-22368-7_20}}}
}

@article{Wacker2019,
  title={Two variants of magnetic diffusivity stabilized finite element methods for the magnetic induction equation},
  author={Wacker, Benjamin},
  journal={Mathematical Methods in the Applied Sciences},
  year={2019},
  publisher={Wiley Online Library}
}

@incollection{VogtEtAl2019,
author={Vogt, Thomas and Strekalovskiy, Evgeny and Cremers, Daniel and Lellmann, Jan},
title={Lifting methods for manifold-valued variational problems},
series={Springer Handbooks},
booktitle={Variational Methods for Nonlinear Geometric Data and Applications},
editor={Grohs, Philipp and Holler, Martin and Weinmann, Andreas},
publisher={Springer International Publishing},
pages={In press},
year={2019},
url={{{https://arxiv.org/pdf/1908.03776.pdf}}},
doi={{{10.1007/tba}}}
}

@INPROCEEDINGS{vonHaxthausen2019,
    author = {von Haxthausen, Felix and J{\"{a}}ckle, Sonja and Strehlow, Jan and Ernst, Floris and  Garc{\'i}a-V{\'a}zquez, Ver{\'o}nica},
     title = {Catheter pose-dependent virtual angioscopy images visualized on augmented reality glasses},
   journal = {Current Directions in Biomedical Engineering},
    volume = {5},
    number = {1},
      year = {2019},
     keywords = {NavEVAR},
     pages = {289-291},
     url={https://www.degruyter.com/view/j/cdbme.2019.5.issue-1/cdbme-2019-0073/cdbme-2019-0073.xml}
}

@INPROCEEDINGS{kraft2019,
     author = {Kraft, Valentin and Strehlow, Jan and J{\"{a}}ckle, Sonja and Garc{\'i}a-V{\'a}zquez, Ver{\'o}nica and Link, Florian and von Haxthausen, Felix and Schenk, Andrea and Schumann, Christian},
      month = sep,
      title = {A comparison of streaming methods for the Microsoft HoloLens},
  booktitle = {Tagungsgband der 18. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)},
       year = {2019},
      pages = {212-216},
  publisher = {Oliver Burgert, Hochschule Reutlingen, Bernhard Hirt, Universit{\"a}t T{\"u}bingen},
   location = {Reutlingen (Germany)},
       isbn = {978-3-00-063717-9}
}

@incollection{jaeckle2019shape,
  title={Shape Sensing with Fiber Bragg Grating Sensors},
  author={J{\"a}ckle, Sonja and Strehlow, Jan and Heldmann, Stefan},
  booktitle={Bildverarbeitung f{\"u}r die Medizin 2019},
  pages={258--263},
  year={2019},
  publisher={Springer},
  keywords = {NavEVAR},
  url={https://link.springer.com/chapter/10.1007%2F978-3-658-25326-4_58},
}

@Book{LellmannBurgerModersitzki2019,
  title =     {Proceedings of the 7th International Conference On Scale Space and Variational Methods in Computer Vision (SSVM 2019)},
  publisher = {Springer},
  year =      {2019},
  author = {Lellmann, J. and Burger, M. and Modersitzki, J. (eds.)},
  series =    {Lecture Notes in Computer Science}
}

@InProceedings{BudelmannPapenbergLellmann2018,
  author =    {Budelmann, Daniel and K{\"o}nig, Lars and Papenberg, Nils and Lellmann, Jan},
  title =     {Fully-Deformable 3D Image Registration in Two Seconds},
  booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019},
  year =      {2019},
  editor =    {Handels, Heinz and Deserno, Thomas M. and Maier, Andreas and Maier-Hein, Klaus Hermann and Palm, Christoph and Tolxdorff, Thomas},
  pages =     {302--307},
  address =   {Wiesbaden},
  publisher = {Springer Fachmedien Wiesbaden}
}

@article{OliverEtAl2019,
author = {Klein, Oliver and Kanter, Frederic and Kulbe, Hagen and Jank, Paul and Denkert, Carsten and Nebrich, Grit and Schmitt, Wolfgang D. and Wu, Zhiyang and Kunze, Catarina A. and Sehouli, Jalid and Darb-Esfahani, Silvia and Braicu, Ioana and Lellmann, Jan and Thiele, Herbert and Taube, Eliane T.},
title = {MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods},
journal = {PROTEOMICS – Clinical Applications},
volume = {13},
number = {1},
pages = {1700181},
doi = {10.1002/prca.201700181},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prca.201700181},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/prca.201700181},
abstract = {Purpose Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. Experimental design Formalin-fixed-paraffin-embedded tissue of 20 patients with ovarian clear-cell, 14 low-grade serous, 19 high-grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI-Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM-lin) and radial basis function kernels (SVM-rbf), a neural network (NN), and a convolutional neural network (CNN). Results MALDI-Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80\% for LDA, 80\% SVM-lin, 74\% SVM-rbf, 83\% NN, and 85\% CNN. Based on sensitivity (69–100\%) and specificity (90–99\%), CCN and NN are most suited to EOC classification. Conclusion and clinical relevance The pilot study demonstrates the potential of MALDI-Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.},
year = {2019}
}

@article{hering2019memory,
  title={Memory-efficient 2.5 D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans},
  author={Hering, Alessa and Kuckertz, Sven and Heldmann, Stefan and Heinrich, Mattias P},
  journal={International journal of computer assisted radiology and surgery},
  volume={14},
  number={11},
  pages={1901--1912},
  year={2019},
  publisher={Springer}
}

@incollection{hering2019BVM,
  title={Enhancing label-driven deep deformable image registration with local distance metrics for state-of-the-art cardiac motion tracking},
  author={Hering, Alessa and Kuckertz, Sven and Heldmann, Stefan and Heinrich, Mattias P},
  booktitle={Bildverarbeitung f{\"u}r die Medizin 2019},
  pages={309--314},
  year={2019},
  publisher={Springer},
  url={http://dx.doi.org/10.1007%2F978-3-658-25326-4_69},
}

@inproceedings{hering2019SPIE,
author = {Hering, Alessa and Heldmann, Stefan},
title = {{Unsupervised learning for large motion thoracic CT follow-up registration}},
volume = {10949},
booktitle = {Medical Imaging 2019: Image Processing},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {1-7},
year = {2019},
doi = {10.1117/12.2506962},
URL = {https://doi.org/10.1117/12.2506962}
}

@inproceedings{hering2019MIDL,
author = {Meine, Hans and Hering, Alessa},
title = {{Efficient Prealignment of CT Scans for Registration through a Bodypart Regressor}},
volume = {102},
booktitle = {Proceedings of Medical Imaging with Deep Learning (MIDL 2019)},
pages = {1-4},
year = {2019},
}

@inproceedings{hering2019MICCAI,
author = {Hering, Alessa and van Ginneken, Bram and Heldmann, Stefan},
title = {{mlVIRNET: Multilevel Variational Image Registration Network}},
volume = {11769},
booktitle = {Proceeding of Medical Image Computing and Computer Assisted Intervention (MICCAI 2019)},
publisher = {Springer},
pages = {257-265},
year = {2019},
doi = {10.1007/978-3-030-32226-7_29},
URL = {https://doi.org/10.1007/978-3-030-32226-7_29}
}

@article{Lotz2019,
  author={Bulten, Wouter and Bándi, Péter and Hoven, Jeffrey and van de Loo, Rob and Lotz, Johannes and Weiss, Nick and van der Laak, Jeroen and van Ginneken, Bram and Hulsbergen-van de Kaa, Christina and Litjens, Geert },
  title={Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard},
  journal={Scientific Reports},
  year={2019},
  volume={9},
  article={864},
  url={https://doi.org/10.1038/s41598-018-37257-4},
}

@article{meine2019efficient,
  title={Efficient Prealignment of CT Scans for Registration through a Bodypart Regressor},
  author={Meine, Hans and Hering, Alessa},
  journal={arXiv preprint arXiv:1909.08898},
  year={2019}
}

@incollection{CHEN2019579,
title = {Chapter 15 - Image and surface registration},
editor = {Ron Kimmel and Xue-Cheng Tai},
series = {Handbook of Numerical Analysis},
publisher = {Elsevier},
volume = {20},
pages = {579-611},
year = {2019},
booktitle = {Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2},
issn = {1570-8659},
doi = {https://doi.org/10.1016/bs.hna.2019.07.001},
url = {https://www.sciencedirect.com/science/article/pii/S1570865919300146},
author = {Ke Chen and Lok Ming Lui and Jan Modersitzki},
keywords = {Image registration, Surface registration, Variational models, 35A15, 37E30, 49J40, 49Q05, 62H35, 62M40, 65D18, 80M30}

@inproceedings{brehmer2018novel,
  title={A Novel Similarity Measure for Image Sequences},
  author={Brehmer, Kai and Wacker, Benjamin and Modersitzki, Jan},
  booktitle={International Workshop on Biomedical Image Registration},
  pages={47--56},
  year={2018},
  organization={Springer},
  url={{{https://arxiv.org/abs/1907.09741}}},
  doi={{{10.1007/978-3-319-92258-4_5}}}
}

@phdthesis{2018-PhD-Konig,
  Author = {Lars König},
  Title = {Matrix-free approaches for deformable image registration with large-scale and real-time applications in medical imaging},
  School = {Institute of Mathematics and Image Computing, University of L\"ubeck},
  Year = {2018},
}

@article{garcia2018navigation,
  title={Navigation and visualisation with HoloLens in endovascular aortic repair},
  author={Garc{\'i}a-V{\'a}zquez, Ver{\'o}nica and von Haxthausen, Felix and J{\"a}ckle, Sonja and Schumann, Christian and Kuhlemann, Ivo and Bouchagiar, Juljan and H{\"o}fer, Anna-Catharina and Matysiak, Florian and H{\"u}ttmann, Gereon and Goltz, Jan Peter and others},
  journal={Innovative Surgical Sciences},
  volume={3},
  number={3},
  pages={167--177},
  year={2018},
  publisher={De Gruyter},
  keywords={NavEVAR},
  url={https://degruyter.com/downloadpdf/j/iss.2018.3.issue-3/iss-2018-2001/iss-2018-2001.pdf}
}

@incollection{jaeckle2018rigid,
  title={Rigid Lens--Locally Rigid Approximations of Deformable Registration for Change Assessment in Thorax-Abdomen CT Follow-Up Scans},
  author={J{\"a}ckle, Sonja and Heldmann, Stefan},
  booktitle={Image Analysis for Moving Organ, Breast, and Thoracic Images},
  pages={272--283},
  year={2018},
  publisher={Springer},
  url={https://link.springer.com/chapter/10.1007/978-3-030-00946-5_27},
}

@article{loewenhauser2018functional,
  title={Functional Lifting for Variational Problems with Higher-Order},
  author={Loewenhauser, Benedikt and Lellmann, Jan},
  journal={Imaging, Vision and Learning Based on Optimization and PDEs: IVLOPDE, Bergen, Norway, August 29--September 2, 2016},
  pages={101},
  year={2018},
  publisher={Springer},
  URL={https://link.springer.com/chapter/10.1007%2F978-3-319-91274-5_5}
}

@article{hanson2018estimating,
  title={Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements},
  author={Hanson, Erik A and Sandmann, Constantin and Malyshev, Alexander and Lundervold, Arvid and Modersitzki, Jan and Hodneland, Erlend},
  journal={PloS one},
  volume={13},
  number={7},
  pages={e0200521},
  year={2018},
  publisher={Public Library of Science}
}

@article{mader2018detection,
  title={Detection and localization of spatially correlated point landmarks in medical images using an automatically learned conditional random field},
  author={Mader, Alexander Oliver and Lorenz, Cristian and Bergtholdt, Martin and von Berg, Jens and Schramm, Hauke and Modersitzki, Jan and Meyer, Carsten},
  journal={Computer Vision and Image Understanding},
  volume={176},
  pages={45--53},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{brehmer2018simultaneous,
  title={Simultaneous Registration of Image Sequences – a novel singular value
based images similarity measure},
  author={Brehmer, Kai and Wacker, Benjamin and Modersitzki, Jan},
  booktitle={Proceedings in Applied Mathematics and Mechanics - 89th GAMM Annual Meeting},
  pages={},
  year={2018},
  organization={Wiley}
}

@article{korolev2018image,
  title={Image reconstruction with imperfect forward models and applications in deblurring},
  author={Korolev, Yury and Lellmann, Jan},
  journal={SIAM Journal on Imaging Sciences},
  volume={11},
  number={1},
  pages={197--218},
  year={2018},
  publisher={SIAM}
}

@article{konig2018matrix,
author = {König, Lars and Rühaak, Jan and Derksen, Alexander and Lellmann, Jan},
journal = {SIAM Journal on Scientific Computing},
title = {A matrix-free approach to parallel and memory-efficient deformable image registration},
year = {2018},
volume = {40},
number = {3},
pages = {B858--B888},
url = {https://arxiv.org/abs/1804.10541},
doi = {10.1137/17M1125522}
}

@article{VogtLellmann2018,
author ={Vogt, T. and Lellmann, J.},
publisher={Springer International Publishing},
title={Measure-Valued Variational Models with Applications to Diffusion-Weighted Imaging},
journal={Journal of Mathematical Imaging and Vision},
year={2018},
month={Jun},
day={08},
issn={1573-7683},
doi={10.1007/s10851-018-0827-8},
url={https://arxiv.org/pdf/1710.00798.pdf}
}

@phdthesis{2018-PhD-Polzin,
  Author = {Thomas Polzin},
  Title = {Large Deformation Diffeomorphic Metric Mappings -- Theory, Numerics, and Applications},
  School = {Institute of Mathematics and Image Computing, University of L\"ubeck},
  Year = {2018},
}

@article{rust3robust,
  title={A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images},
  author={Rust, Caterina and H{\"a}ger, Stephanie and Traulsen, Nadine and Modersitzki, Jan},
  journal={Current Directions in Biomedical Engineering},
  volume={3},
  number={2},
  year={2017},
  pages={533--537},
  publisher={De Gruyter}
}

@article{ruthotto2017stabilized,
  title={A stabilized multigrid solver for hyperelastic image registration},
  author={Ruthotto, Lars and Greif, Chen and Modersitzki, Jan},
  journal={Numerical Linear Algebra with Applications},
  volume={24},
  number={5},
  pages={e2095},
  year={2017},
  publisher={Wiley Online Library}
}

@incollection{mader2017detection,
  title={Detection and Localization of Landmarks in the Lower Extremities Using an Automatically Learned Conditional Random Field},
  author={Mader, Alexander Oliver and Lorenz, Cristian and Bergtholdt, Martin and von Berg, Jens and Schramm, Hauke and Modersitzki, Jan and Meyer, Carsten},
  booktitle={Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics},
  pages={64--75},
  year={2017},
  publisher={Springer}
}

@ARTICLE{ruhaak2017matrix,
  author = {R\"{u}haak, Jan and K\"{o}nig, Lars and Tramnitzke, Florian and K\"{o}stler, Harald and Modersitzki, Jan},
  title = {A {M}atrix-{F}ree {A}pproach to {E}fficient {A}ffine-{L}inear {I}mage {R}egistration on {CPU} and {GPU}},
  journal = {{J}ournal of {R}eal-{T}ime {I}mage {P}rocessing},
  year      = {2017},
  volume    = {13},
  number    = {1},
  pages     = {205--225},
  issn      = {1861-8219},
  publisher = {Springer},
  url = {http://dx.doi.org/10.1007/s11554-016-0564-4}
}

@article{ruhaak2017estimation,
  title={Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration},
  author={R{\"u}haak, Jan and Polzin, Thomas and Heldmann, Stefan and Simpson, Ivor J. A. and Handels, Heinz and Modersitzki, Jan and Heinrich, Mattias P.},
  journal={IEEE Transactions on Medical Imaging},
  volume = {36},
  number = {8},
  pages = {1746--1757},
  year={2017},
  publisher={IEEE}
}

@phdthesis{2017-PhD-Sandmann,
  Author = {Constantin M. Sandmann},
  Title = {Dynamic Models and Coupling in Medical 4D Imaging},
  School = {Institute of Mathematics and Image Computing, University of L\"ubeck},
  Year = {2017},
}

@INPROCEEDINGS{vogt2017qball,
author={Vogt, Thomas and Lellmann, Jan},
title={An Optimal Transport-Based Restoration Method for Q-Ball Imaging},
booktitle={Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings},
pages={271--282},
year={2017},
publisher={Springer International Publishing},
editor={Lauze, Fran{\c{c}}ois and Dong, Yiqiu and Dahl, Anders Bjorholm},
doi={{{10.1007/978-3-319-58771-4_22}}}
}

@article{SandmannEtAl2016,
author = {Constantin Sandmann and Erlend Hodneland and Jan Modersitzki},
title = {A practical guideline for T1 reconstruction from various flip angles in MRI},
journal = {Journal of Algorithms \& Computational Technology},
volume = {10},
number = {4},
pages = {213-223},
year = {2016},
URL = {http://dx.doi.org/10.1177/1748301816656288},
abstract = { A fast and robust method for T1 estimation in MRI is the so-called variable flip angle technique. We introduce a novel family of T1 reconstruction methods from data acquired with various flip angles and propose a family member which combines the robustness of a nonlinear- with the computational advantages of a linear reconstruction. The constructed family contains the most common approaches for T1 estimation, namely a linear and a nonlinear approach. A general sensitivity analysis for arbitrary members of the family is established. Advantages of the optimized reconstruction are demonstrated on phantom- as well as real data, showing improvements of up to 24\% as compared with the linear method. As a further means to stabilize T1 estimation, spatial stabilization methods are compared. We demonstrate on phantom and on real data that improved results can be obtained if not only T1 but also a second unknown M0 in the reconstruction is stabilized. }
}

@INPROCEEDINGS{PolzinEtAl2016,
  author = {Polzin, Thomas and Niethammer, Marc and Heinrich, Mattias Paul and
	Handels, Heinz and Modersitzki, Jan},
  title = {{Memory Efficient LDDMM for Lung CT}},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention 2016},
  pages = {28--36},
  year = {2016},
  publisher = {Springer},
 editor = {Sebastién Ourselin and Leo Joskowicz and Mert R. Sabuncu and Gozde Unal and William Wells}
}

@article{konig2016deformable,
  title={Deformable image registration for adaptive radiotherapy with guaranteed local rigidity constraints},
  author={K{\"o}nig, Lars and Derksen, Alexander and Papenberg, Nils and Haas, Benjamin},
  journal={Radiation Oncology},
  volume={11},
  number={1},
  pages={122},
  year={2016},
  publisher={BioMed Central},
doi={10.1186/s13014-016-0697-4}
}

@article{stutzer2016evaluation,
  title={Evaluation of a deformable registration algorithm for subsequent lung computed tomography imaging during radiochemotherapy},
  author={St{\"u}tzer, Kristin and Haase, Robert and Lohaus, Fabian and Barczyk, Steffen and Exner, Florian and L{\"o}ck, Steffen and R{\"u}haak, Jan and Lassen-Schmidt, Bianca and Corr, D{\"o}rte and Richter, Christian},
  journal={Medical Physics},
  volume={43},
  number={9},
  pages={5028--5039},
  year={2016},
  publisher={Wiley Online Library}
}

@InProceedings{Laude2016,
  author = {Laude, E. and M{\"o}llenhoff, T. and M{\"o}ller, M. and Lellmann, J and Cremers, D.},
  title =  {Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies},
  year =   {2016},
  booktitle  = {Proc. Europ. Conf. Comp. Vision}
}

@article{derksen2016joint,
  title={A Joint Registration and Segmentation Approach for Large Bladder Deformations in Adaptive Radiotherapy},
  author={Derksen, A and König, L and Meine, H and Heldmann, S},
  journal={Medical Physics: Proceedings of the AAPM 58th annual meeting, Washington, DC},
  volume={43},
  number={6},
  pages={3429--3429},
  year={2016},
  doi={10.1118/1.4956005},
  publisher={American Association of Physicists in Medicine}
}

@Article{LeeCaiLellmannEtAl2016,
  author =  {Lee, J. and Cai, X. and Lellmann, J. and Dalponte, M. and Malhi, Y. and Butt, N. and Morecroft, M. and Schönlieb, C.-B. and Coomes, D.},
  title =   {Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust {PCA}},
  journal = {Appl. Earth Obs. Remote Sensing},
  year =    {2016},
  note =    {In Press.}
}

@article{derksen2016alternating,
  title={An Alternating Image Registration Approach for Large Scale Bladder Deformations in Radiation Therapy},
  author={Derksen, A and König, L and Meine, H},
  journal={18th International Conference on the use of Computers in Radiation Therapy (ICCR), London},
  year={2016},
}

@InProceedings{Moellenhoff2016,
  author =    {M{\"o}llenhoff, T. and Laude, E. and M{\"o}ller, M. and Lellmann, J. and Cremers, D.},
  title =     {Sublabel-Accurate Relaxation of Nonconvex Energies},
  booktitle = {Proc. IEEE Conf. Comp. Vis. Patt. Recogn.},
  year =      {2016}
}

@article{Lotz2016Integration,
abstract = {In the last years, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) became an imaging technique which has the potential to characterize complex tumor tissue. The combination with other modalities and with standard histology techniques was achieved by the use of image registration methods and enhances analysis possibilities. We analyzed an oral squamous cell carcinoma with up to 162 consecutive sections with MALDI MSI, hematoxylin and eosin (H{\&}E) staining and immunohistochemistry (IHC) against CD31. Spatial segmentation maps of the MALDI MSI data were generated by similarity-based clustering of spectra. Next, the maps were overlaid with the H{\&}E microscopy images and the results were interpreted by an experienced pathologist. Image registration was used to fuse both modalities and to build a three-dimensional (3D) model. To visualize structures below resolution of MALDI MSI, IHC was carried out for CD31 and results were embedded additionally. The integration of 3D MALDI MSI data with H{\&}E and IHC images allows a correlation between histological and molecular information leading to a better understanding of the functional heterogeneity of tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.},
author = {Lotz, Judith M. and Hoffmann, Franziska and Lotz, Johannes and Heldmann, Stefan and Trede, Dennis and Oetjen, Janina and Becker, Michael and Ernst, G{\"{u}}nther and Maas, Peter and Alexandrov, Theodore and Guntinas-Lichius, Orlando and Thiele, Herbert and von Eggeling, Ferdinand},
doi = {10.1016/j.bbapap.2016.08.018},
issn = {15709639},
journal = {Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics},
keywords = {3D,Head and Neck Cancer,MALDI imaging,OSCC,image registration,immunohistochemistry,multimodality},
pmid = {27594533},
publisher = {Elsevier B.V.},
title = {{Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition}},
year = {2016}
}

@article{laue2015software,
  title={Software support for combined staging of lung cancer in CT, functional MRI and pathology},
  author={Laue, Hendrik OA and Kohlmann, Peter and Lotz, Johannes and Sedlaczek, Oliver and Mueller, Benedikt and Breuhahn, Kai and Grabe, Niels and Warth, Arne and Hahn, Horst},
  year={2015}
}

@article{gunther2015towards,
  title={Towards MR-guided biopsy outside the MR-scanner},
  author={Günther, M and Bartscherer, T and Georgii, J and Hewener, H and Kipshagen, T and Ojdanic, D and Kocev, B and Lotz, J and Olesch, J and Rothlübbers, S and others},
  year={2015}
}

@INPROCEEDINGS{7582045,
  author={Mannweiler, Dirk and Suhr, Sebastian and Modersitzki, Jan and Burger, Martin},
  booktitle={2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)}, 
  title={Variational method for motion corrected reconstruction with MRI information in positron emission tomography}, 
  year={2015},
  volume={},
  number={},
  pages={1-4},
  keywords={Image reconstruction;Logic gates;Positron emission tomography;Magnetic resonance imaging;Phantoms;Attenuation;Motion estimation},
  doi={10.1109/NSSMIC.2015.7582045}}

@article{horn2015prototyp,
  title={Ein Prototyp f{\"u}r die navigierte Implantation von Aortenstentprothesen zur Reduzierung der Kontrastmittel-und Strahlenbelastung: Das Nav-CARS-EVAR-Konzept (Navigated-Contrast-Agent and Radiation Sparing Endovascular Aortic Repair)},
  author={Horn, Marco and Nolde, Jan and Goltz, Jan Peter and Barkhausen, J{\"o}rg and Schade, W and Waltermann, C and Modersitzki, J and Olesch, J and Papenberg, N and Keck, T and others},
  journal={Zentralblatt f{\"u}r Chirurgie-Zeitschrift f{\"u}r Allgemeine, Viszeral-, Thorax-und Gef{\"a}{\ss}chirurgie},
  volume={140},
  number={05},
  pages={493--499},
  year={2015},
  publisher={Georg Thieme Verlag KG}
}

@article{Lotz2015,
abstract = {Objective: Image Registration of whole slide histology images allows the fusion of fine-grained information - like different immunohistochemical stains - from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell-level, automatic analysis can be used to ease the pathologist's work. However, the size of those images exceeds the memory capacity of regular computers. Methods: We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multi-stain images. Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15 %, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multi-stain registration which allows to compare different anti-bodies at cell-level. available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multi-stain images.Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15 %, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multi-stain registration which allows to compare different anti-bodies at cell-level.},
author = {Lotz, J and Olesch, J and M{\"{u}}ller, B and Polzin, T and Galuschka, P and Lotz, J M and Heldmann, S and Laue, H and Warth, A and Lahrmann, B and Grabe, N and Sedlaczek, O and Breuhahn, K and Modersitzki, J},
doi = {10.1109/TBME.2015.2503122},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {computer-aided diagnosis,digital pathology,high resolution histological scans ,histopathology, image registration},
title = {{Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images}},
year = {2015},
volume = {63}, 
number = {9},
pages = {1812--1819}
}

@Article{Lellmann2015,
  author =      {Lellmann, J. and Papafitsoros, K. and Schönlieb, C. and Spector, D.},
  title =       {Analysis and Application of a non-local Hessian},
  journal =     {SIAM Journal on Imaging Sciences},
  year =        {2015},
  volume =      {8},
  number =      {4},
  pages =       {2161--2202},
  owner =       {Jan},
  timestamp =   {2014.07.02},
}

@article{deluca2015the,
  author={V De Luca and T Benz and S Kondo and L König and D Lübke and S Rothlübbers and O Somphone and S Allaire and M A Lediju Bell and D Y F
Chung and A Cifor and C Grozea and M Günther and J Jenne and T Kipshagen and M Kowarschik and N Navab and J Rühaak and J Schwaab and C
Tanner},
  title={The 2014 liver ultrasound tracking benchmark},
  journal={Physics in Medicine and Biology},
  volume={60},
  number={14},
  pages={5571},
  url={http://dx.doi.org/10.1088/0031-9155/60/14/5571},
  year={2015}
}

@inproceedings{2015-hpdb-SSVM,
author = {Stefan Heldmann and Thomas Polzin and Alexander Derksen and Benjamin Berkels},
title = {An Image Registration Framework for Sliding Motion with Piecewise Smooth Deformations},
booktitle = {Scale Space and Variational Methods in Computer Vision},
year = {2015},
month = {May/June},
abstract = {We present a novel variational framework for image registration with explicit modeling of sliding motion, as it occurs, e.g., in the medical context at organ boundaries. The key of our method is a piecewise smooth deformation model that allows for discontinuities at the sliding interfaces while keeping the sliding domain in contact with its surrounding. The presented approach is generic and can be used with a large class of both image similarity measures and regularizers for the deformations. A useful byproduct of the proposed method is an automatic propagation of a given segmentation from one image to the other. We proof existence of minimizers under rather mild assumptions and present an efficient scheme for computing a numerical solution. The minimization is based on a splitting approach with alternating derivative based Gauss-Newton and fast first order convex optimization. Finally, we evaluate the proposed method on synthetic and real data.},
series = {Lecture Notes in Computer Science},
pages = {335--347},
address = {Lège-Cap Ferret, France},
publisher = {Springer International},
volume = {9087},
editor = {Jean-François Aujol and Mila Nikolova and Nicolas Papadakis},
}

@inproceedings{Chen2015Deformable,
title={Deformable Image Registration with Automatic Non-Correspondence Detection},
author={Chen, Kanglin and Derksen, Alexander and Heldmann, Stefan and Hallmann, Marc and Berkels, Benjamin},
editor={Aujol, Jean-Fran{\c{c}}ois and Nikolova, Mila and Papadakis, Nicolas},
booktitle={Scale Space and Variational Methods in Computer Vision: 5th International Conference, SSVM 2015, Lège-Cap Ferret, France, May 31 - June 4, 2015, Proceedings},
year={2015},
publisher={Springer International Publishing},
address={Cham},
pages={360--371},
isbn={978-3-319-18461-6},
doi={10.1007/978-3-319-18461-6_29},
url={http://dx.doi.org/10.1007/978-3-319-18461-6_29}
}

@article{pompe2015parametric,
  title={Parametric Response Mapping Adds Value to Current Computed Tomography Biomarkers in Diagnosing Chronic Obstructive Pulmonary Disease},
  author={Pompe, Esther and van Rikxoort, Eva M and Schmidt, Michael and R{\"u}haak, Jan and Estrella, Leticia Gallardo and Vliegenthart, Rozemarijn and Oudkerk, Matthijs and de Koning, Harry J and van Ginneken, Bram and de Jong, Pim A and Lammers, Jan-Willem J and Hoesein, Firdaus A A Mohamed},
  journal={American {J}ournal of {R}espiratory and {C}ritical {C}are {M}edicine},
  volume={191},
  number={9},
  pages={1084--1086},
  year={2015},
  url={http://www.atsjournals.org/doi/abs/10.1164/rccm.201411-2105LE#.VebzExG8PRY},
  publisher={Am Thoracic Soc}
}

@article{konig2015deformable,
  title={Deformable image registration with guaranteed local rigidity},
  author={K{\"o}nig, L and Derksen, A and Heldmann, S and Papenberg, N and Modersitzki, J and Haas, B},
  journal={Radiotherapy and Oncology: Proceedings of the 3rd ESTRO Forum, Barcelona},
  volume={115},
  pages={S197--S198},
  year={2015},
  publisher={Elsevier},
doi={10.1016/S0167-8140(15)40405-0}
}

@INPROCEEDINGS{konig2015parallel,
	author = {K\"{o}nig, Lars and Derksen, Alexander and Hallmann, Marc and Papenberg, Nils},
	booktitle = {IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	title = {Parallel and Memory Efficient Multimodal Image Registration for Radiotherapy using Normalized Gradient Fields},
	year = {2015},
	address = {New York City, USA},
	month = {April},
        url = {http://dx.doi.org/10.1109/ISBI.2015.7163977}
}

@INPROCEEDINGS{2015-BVM-CD,
	author = {Chen, Kanglin and Derksen, Alexander},
	booktitle = {Workshop: Bildverarbeitung für die Medizin},
	title = {A Variational Method for Contructing Unbiased Atlas with Probabilistic Label Maps},
	year = {2015},
	address = {},
	month = {}	
}

@INPROCEEDINGS{papenberg2015locally,
	author = {Papenberg, Nils and König, Lars and Heldmann, Stefan and Derksen, Alexander and Modersitzki, Jan and Haas, Benjamin},
	booktitle = {Varian Research Partnership Symposium},
	title = {A Locally Variable Transformation Model for Deformable Image Registration Using Tissue Properties},
	year = {2015},
	address = {Atlanta, Georgia, USA},
	month = {March}	
}

@inproceedings{Weiss2015,
abstract = {Abstract. 3D reconstruction and digital double staining offer pathologists many new insights into tissue structure and metabolism. Key to these applications is the precise registration of histological slide images, that is challenging in several ways. One major challenge are differently stained slides, that highlight different parts of the tissue. In this paper we introduce a new registration method to face this multimodality. It abstracts the image information to cell nuclei densities. By minimizing the distance of these densities an affine transformation is determined that restores the lost spatial correspondences. The proposed density based registration is evaluated using consecutive histological slides. It is compared to a Mutual Information based registration and shown to be more accurate and robust.},
address = {Berlin, Heidelberg},
author = {Weiss, Nick and Lotz, Johannes and Modersitzki, Jan},
booktitle = {Bildverarbeitung f\"{u}r die Medizin 2015, Algorithmen – Systeme – Anwendungen, Proceedings des Workshops vom 15. bis 17. M\"{a}rz 2015 in L\"{u}beck},
doi = {10.1007/978-3-662-46224-9},
editor = {Handels, Heinz and Deserno, Thomas M. and Meinzer, Hans-Peter and Tolxdorff, Thomas},
file = {:home/jo/Seafile/misc/mendeley/Weiss, Lotz, Modersitzki - 2015 - Multimodal Image Registration in Digital Pathology Using Cell Nuclei Densities.pdf:pdf},
isbn = {9783662462232},
month = mar,
pages = {245},
publisher = {Springer Vieweg},
title = {{Multimodal Image Registration in Digital Pathology Using Cell Nuclei Densities}},
year = {2015}
}

@inproceedings{2015-BVM-dhpb-sliding,
  title={Image registration with sliding motion constraints for 4D CT motion correction},
  author={Derksen, Alexander and Heldmann, Stefan and Polzin, Thomas and Berkels, Benjamin},
  pages={335--340},
  year={2015},
  publisher={Springer},
  address = {Berlin, Heidelberg},
  booktitle = {Bildverarbeitung f\"{u}r die Medizin 2015, Algorithmen – Systeme –   Anwendungen, Proceedings des Workshops vom 15. bis 17. M\"{a}rz 2015 in L\"{u}beck},
  editor = {Handels, Heinz and Deserno, Thomas M. and Meinzer, Hans-Peter and Tolxdorff, Thomas},
  isbn = {9783662462232},
  month = March,
  url = {http://dx.doi.org/10.1007/978-3-662-46224-9_58},
  series = {Informatik aktuell},
  abstract = {A common assumption in medical image registration is that the estimation of a globally continuous deformation field is plausible in reality. However, a sliding behavior of adjacent organ boundaries (e.g. lung and ribcage) cannot be described in a plausible way by a continuous deformation field. In this paper, we address this issue with a novel registration framework that explicitly models sliding of interfaces and can preserve discontinuities in the deformation field along predefined organ boundaries. Incorporated methods involve constrained nonlinear registration and a finite element discretization on unstructured tetrahedral meshes. Evaluation is based on the freely available DIR-Lab datasets.}
}

@Article{Kappes2015,
  Title                    = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems},
  Author                   = {Kappes, J.~H. and Andres, B. and Hamprecht, F. A. and Schn\"orr, C. and Nowozin, S. and Batra, D. and Kim, S. and Kausler, B. X. and Kröger, T and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Rother, C.},
  Journal                  = {Int.~J.~Comp.~Vis},
  Year                     = {2015},
  Note                     = {(online first)},
}

@INPROCEEDINGS{2015-SPIE-WSRDDLH,
	author = {Wang, Lei and Strehlow, Jan and R\"{u}haak, Jan and Diez, Yago and Diekmann, Susanne and Laue, Hendrik and Hahn, Horst K.},
	booktitle = {SPIE Medical Imaging 2015: Image Processing},
	title = {A fast alignment method for breast MRI follow-up studies using automated breast segmentation and current-prior registration},
	year = {2015},
	address = {Orlando, Florida, USA},
	month = {February}	
}

@INPROCEEDINGS{2015-SPIE-RDHHM,
	author = {R\"{u}haak, Jan and Derksen, Alexander and Heldmann, Stefan and Hallmann, Marc and Meine, Hans},
	booktitle = {SPIE Medical Imaging 2015: Image Processing},
	title = {Accurate CT-MR image registration for deep brain stimulation: a multi-observer evaluation study},
	year = {2015},
	address = {Orlando, Florida, USA},
	month = {February}	
}

@incollection{Modersitzki2015307,
title = {Nonlinear Registration Via Displacement Fields},
editor = {Toga, Arthur W.},
booktitle = {Brain Mapping},
publisher = {Academic Press},
edition = {},
address = {Waltham},
year = {2015},
pages = {307 - 314},
isbn = {978-0-12-397316-0},
doi = {http://dx.doi.org/10.1016/B978-0-12-397025-1.00300-6},
url = {http://www.sciencedirect.com/science/article/pii/B9780123970251003006},
author = {J. Modersitzki and S. Heldmann and N. Papenberg},
keywords = {Displacement fields, Image fusion,Image registration, Nonlinear registration, Variational formulation},
abstract = {Abstract This article presents a brief review on nonlinear registration techniques based on a variational formulation. The main advantages of this setting are its great modeling potential and its modular setting. This enables easy changes and adaptations for fine-tuning for particular applications. Furthermore, this setting also enables the integration of additional information. This can be formulated in terms of either soft or hard constraints, such as privileged handling of anatomical landmarks, local rigidity of structures, and emphasis on volume preservation or limitations of volume changes. The latter is of particular interest for one-to-one displacement fields (also known as diffeomorphic registration). The setting is supported by a rigorous mathematical theory and can be translated into fast and stable implementations. Although the focus of this article is not on the practical realization, important concepts such as multiscale and multilevel representations and reduction of ambiguities of results based on proper regularization are also briefly discussed.},
}

@article{kowallick2014quantification,
  title={Quantification of left ventricular torsion and diastolic recoil using cardiovascular magnetic resonance myocardial feature tracking},
  author={Kowallick, Johannes T and Lamata, Pablo and Hussain, Shazia T and Kutty, Shelby and Steinmetz, Michael and Sohns, Jan M and Fasshauer, Martin and Staab, Wieland and Unterberg-Buchwald, Christina and Bigalke, Boris and others},
  journal={PloS one},
  volume={9},
  number={10},
  pages={e109164},
  year={2014},
  publisher={Public Library of Science San Francisco, USA}
}

@article{QIMS4050,
	author = {Shuo Zhang and Arun A. Joseph and Dirk Voit and Sebastian Schaetz and Klaus-Dietmar Merboldt and Christina Unterberg-Buchwald and Anja Hennemuth and Joachim Lotz and Jens Frahm},
	title = {Real-time magnetic resonance imaging of cardiac function and flow—Recent progress},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {4},
	number = {5},
	year = {2014},
	keywords = {},
	abstract = {Cardiac structure, function and flow are most commonly studied by ultrasound, X-ray and magnetic resonance imaging (MRI) techniques. However, cardiovascular MRI is hitherto limited to electrocardiogram (ECG)-synchronized acquisitions and therefore often results in compromised quality for patients with arrhythmias or inabilities to comply with requested protocols—especially with breath-holding. Recent advances in the development of novel real-time MRI techniques now offer dynamic imaging of the heart and major vessels with high spatial and temporal resolution, so that examinations may be performed without the need for ECG synchronization and during free breathing. This article provides an overview of technical achievements, physiological validations, preliminary patient studies and translational aspects for a future clinical scenario of cardiovascular MRI in real time.},
	issn = {2223-4306},	url = {https://qims.amegroups.org/article/view/4050}
}

@ARTICLE{2014-MIM-PRWHM, 
  author = {Polzin, Thomas and R\"{u}haak, Jan and Werner, Ren\'{e} and Handels, Heinz and Modersitzki, Jan},
  title = {Lung Registration using Automatically Detected Landmarks},
  journal = {Methods of Information in Medicine},
  year = {2014},
  volume={53},
  pages={250--256}
}

@article{thiele20142d,
  title={2D and 3D MALDI-imaging: conceptual strategies for visualization and data mining},
  author={Thiele, Herbert and Heldmann, Stefan and Trede, Dennis and Strehlow, Jan and Wirtz, Stefan and Dreher, Wolfgang and Berger, Judith and Oetjen, Janina and Kobarg, Jan Hendrik and Fischer, Bernd and others},
  journal={Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics},
  volume={1844},
  number={1},
  pages={117--137},
  year={2014},
  publisher={Elsevier}
}

@ARTICLE{2014-JRTIP-BKRLF,
  author = {Berg, Roelof and K\"{o}nig, Lars and R\"{u}haak, Jan and Lausen, Ralph and Fischer, Bernd},
  title = {{H}ighly {E}fficient {I}mage {R}egistration for {E}mbedded {S}ystems
	{U}sing a {D}istributed {M}ulticore {DSP} {A}rchitecture},
  journal = {{J}ournal of {R}eal-{T}ime {I}mage {P}rocessing},
  year = {2014},
  month = {November},
  publisher = {Springer},
  url = {http://dx.doi.org/10.1007/s11554-014-0457-3}
}

@INPROCEEDINGS{2014-DBS-CHRH,
	author = {Chen, Kanglin and Heldmann, Stefan and R\"{u}haak, Jan and Hallmann, Marc},
	booktitle = {Proc. MICCAI 2014 Workshop on Deep Brain Stimulation Methodological Challenges - 2nd Edition},
	title = {Construction of Average STN Atlas using Image Registration and Reconstruction},
	year = {2014},
	address = {Boston, USA},
	month = {September}	
}

@INPROCEEDINGS{2014-CLUST-KKR,
	author = {K\"{o}nig, Lars and Kipshagen, Till and R\"{u}haak, Jan},
	booktitle = {Proc. MICCAI Challenge on Liver Ultrasound Tracking (CLUST 2014)},
	title = {A Non-Linear Image Registration Scheme for Real-Time Liver Ultrasound Tracking using Normalized Gradient Fields},
	year = {2014},
	address = {Boston, USA},
	month = {September},
pages = {29--36}
}

@INPROCEEDINGS{2014-MICCAI-HPC-TRKMK,
  author = {Tramnitzke, Florian and R\"{u}haak, Jan and K\"{o}nig, Lars and Modersitzki, Jan and K\"{o}stler, Harald},
  title = {{GPU} {B}ased {A}ffine {L}inear {I}mage {R}egistration
using {N}ormalized {G}radient {F}ields},
  booktitle = {Proc. Seventh International Workshop on High Performance Computing for Biomedical Image Analysis (HPC-MICCAI)},
  year = {2014},
  address = {Boston, USA},
  month = {September},
pages = {5--14}
}