Ullrich Köthe

[ Scientific Interests | VIGRA | CV | Publications | Teaching ]

Prof. Dr. Ullrich Köthe

Associate Professor
Group Leader in the Visual Learning Lab Heidelberg
Mathematikon A, office 4/334

Email: ullrich.koethe (at) iwr.uni-heidelberg.de
Phone: +49 6221 54 14834
Fax: +49-6221-54 5276
Address:
Interdisciplinary Center for Scientific Computing (IWR)
University of Heidelberg
Im Neuenheimer Feld 205
69120 Heidelberg

Scientific Interests

I’m heading the newly founded group on “Explainable Machine Learning”. We are interested in the connection between machine learning and its applications in image analysis, medicine, and natural sciences. Explainable learning shall open-up the blackbox of successful learning algorithms, in particular neural networks, to provide insight rather than mere numbers.

In addition, I’m interested in generic software bringing state-of-the-art algorithms to the end user and maintain the VIGRA image analysis library.

Teaching

By individual arrangement

  • Master and bachelor theses in the field of machine learning and image analysis (Informatik, Scientific Computing, Physics)
  • Practicals, creditable for e.g. BSc Informatik (IFP), MSc Informatik (IFM), Physics (WPProj)

Winter Term 2018/2019

Previous semesters

Selected Publications

Please refer to my profile at Google Scholar and the DBLP Citation Database for a more complete list. My pre-2010 publications can also be found here.

Theses:

  • U. Köthe: “Reliable Low-Level Image Analysis”
    Habilitation Thesis, Department Informatik, University of Hamburg, 318 pages, Hamburg 2008
    Abstract | PDF (10 MB)

  • U. Köthe: “Generische Programmierung für die Bildverarbeitung”
    PhD Thesis, Fachbereich Informatik, Universität Hamburg, 274 pages, Hamburg 2000, ISBN: 3-8311-0239-2. (in German)
    Abstract | PDF (12.5 MB)

Recent and popular papers:

  • L. Ardizzone, J. Kruse, S. Wirkert, D. Rahner, E.W. Pellegrini, R.S. Klessen, L. Maier-Hein, C. Rother, U. Köthe:
    “Analyzing Inverse Problems with Invertible Neural Networks”
    arXiv:1808.04730, 2018.
    Abstract | PDF

  • S. Wolf, C. Pape, A. Bailoni, N. Rahaman, A. Kreshuk, U. Köthe, F.A. Hamprecht:
    “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”
    in: Europ. Conf. Computer Vision (ECCV’18), pp. 546-562 , 2018.
    Abstract | PDF

  • S. Wolf, L. Schott, U. Köthe, F.A. Hamprecht:
    “Learned Watershed: End-to-End Learning of Seeded Segmentation”
    in: Intl. Conf. Computer Vision (ICCV’17), pp. 2030-2038, 2017.
    Abstract | PDF

  • C. Sommer, C. Straehle, U. Köthe, F.A. Hamprecht:
    “ilastik: Interactive learning and segmentation toolkit”
    In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 230-233, 2011.
    Abstract | PDF

  • U. Köthe: “Edge and Junction Detection with an Improved Structure Tensor”
    in: B. Michaelis, G. Krell (Eds.): Pattern Recognition, Proc. of 25th DAGM Symposium, Magdeburg 2003, Springer LNCS 2781, pp. 25-32, 2003.
    Abstract | PDFAwarded the main prize of the German Pattern Recognition Society (DAGM) 2003

  • U. Köthe: “Integrated Edge and Junction Detection with the Boundary Tensor”
    in: ICCV ‘03, Proc. of 9th Intl. Conf. on Computer Vision, Nice 2003, vol. 1, pp. 424-431, 2003.
    Abstract | PDF

  • B. Andres, U. Köthe, M. Helmstaedter, W. Denk, F.A. Hamprecht:
    “Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification”
    in: G. Rigoll (Ed.): Pattern Recognition, Proc. DAGM 2008, Springer LNCS 5096 , pp. 142-152, 2008.
    Abstract | BibTeX | PDFReceived a Best Paper Award from the German Association for Pattern Recognition (DAGM)

  • B. Andres, T. Kröger, K. Briggmann, W. Denk, N. Norogod, G. Knott, U. Köthe, F.A. Hamprecht:
    “Globally Optimal Closed-Surface Segmentation for Connectomics”
    in: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (Eds.) : 12th Eur. Conf. Computer Vision (ECCV 2012) part III, Springer LNCS 7574, pp. 778-791, 2012.
    Abstract | BibTeX | PDF

  • T. Beier, B. Andres, U. Köthe, F.A. Hamprecht:
    “An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”
    in: Leibe, B., Matas, J., Sebe, N., Welling, M. (Eds.) : 14th Eur. Conf. Computer Vision (ECCV 2016), 2016.
    Abstract | PDF

  • U. Köthe: “Reusable Software in Computer Vision”
    in: B. Jähne, H. Haussecker, P. Geissler (Eds.): Handbook of Computer Vision and Applications, Volume 3: Systems and Applications, pp. 103-132, San Diego: Academic Press, 1999.
    PDF
  • U. Köthe, M. Felsberg:
    “Riesz-Transforms Versus Derivatives: “On the Relationship Between the Boundary Tensor and the Energy Tensor”
    in: R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale Space and PDE Methods in Computer Vision, Springer LNCS 3459, pp. 179-191, 2005.
    Abstract | PDF

  • A. Kreshuk, U. Köthe, E. Pax, D. Bock, F.A. Hamprecht:
    “Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks”
    PLoS ONE 9(2): e87351, 2014.
    Abstract | BibTeX | PDF

  • B. Kausler, M. Schiegg, B. Andres, M. Lindner, U. Köthe, H. Leitte, J. Wittbrodt, L. Hufnagel, F.A. Hamprecht:
    “A discrete chain graph model for 3D+t cell tracking with high misdetection robustness”
    in: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (Eds.) : 12th Eur. Conf. Computer Vision (ECCV 2012) part III, Springer LNCS 7574, pp. 144-157, 2012.
    Abstract | BibTeX | PDF

  • M. Hanselmann, U. Köthe, M. Kirchner, B.Y. Renard, E.R. Amstalden, K. Glunde, R.M.A. Heeren, F.A. Hamprecht:
    “Towards Digital Staining using Imaging Mass Spectrometry and Random Forests”
    Journal of Proteome Research, 8(7):3558-3567, 2009
    Abstract | BibTeX | PDF

  • B. Menze, B. Kelm, N. Splitthoff, U. Köthe, F.A. Hamprecht:
    “On oblique random forests”
    in: Mach. Learning and Knowledge Discovery in Databases, Springer LNCS 6912, pp. 453-469, 2011.
    Abstract | PDF

  • U. Köthe, F. Herrmannsdörfer, I. Kats, F.A. Hamprecht:
    “SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy”
    Histochemistry and Cell Biology, 141(6):613–627, 2014.
    Abstract | PDF

  • H. Meine, U. Köthe, P. Stelldinger:
    “A Topological Sampling Theorem for Robust Boundary Reconstruction and Image Segmentation”
    Discrete Applied Mathematics (DGCI Special Issue), 157(3):524-541, 2009.
    Abstract | PDF

  • U. Köthe: “What Can We Learn from Discrete Images about the Continuous World?”
    in: D. Coeurjolly, I. Sivignon, L. Tougne, F. Dupont (Eds.): Discrete Geometry for Computer Imagery, Proc. DGCI 2008, Springer LNCS 4992, pp. 4-19, 2008.
    Abstract | PDF

  • P. Stelldinger, U. Köthe:
    “Towards a general sampling theory for shape preservation”
    Image and Vision Computing, Special Issue Discrete Geometry for Computer Vision, 23(2): 237-248, 2005.
    Abstract | PDF

Curriculum Vitae

since 2018 Associate Professor and group leader in the Visual Learning Lab Heidelberg
26. Nov. 2008 Habilitation for a thesis entitled “Reliable Low-Level Image Analysis”, Department of Informatics, University of Hamburg
2008-2012 Senior scientist at Heidelberg Collaboratory for Image Processing (HCI)
2007-2017 Vice Group Leader of the Image Analysis ans Learning Group (formerly: Multidimensional Image Processing), University of Heidelberg
Spring semester 2004 Guest researcher at Computer Vision Laboratory, Linköping University, Sweden
1999-2007 Assistant professor (officially: “Hochschulassistent”) in the Cognitive Systems Group, University of Hamburg
29. Feb. 2000 Dr. rer. nat. (PhD) for a thesis entitled “Generische Programmierung für die Bildverarbeitung”, Department of Informatics, University of Hamburg
Spring semester 1993 Guest researcher at Sarnoff Corporation, Princeton, USA
1992-1999 Research assistant at the Fraunhofer Institute for Computer Graphics, Rostock
1986-1991 Study of physics at University of Rostock, Diploma thesis on “Mikroskopische Herleitung einer Ratengleichung am Beispiel der Nukleonen-Deuteron-Reaktion”