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Techreport
U. Köthe, “Reliable Low-Level Image Analysis”, Habilitation thesis. Department Informatik, University of Hamburg, Hamburg, 2008.PDF icon Technical Report (12.44 MB)
J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.
Journal Article
J. Kleesiek, Petersen, J., Döring, M., Maier-Hein, K., Köthe, U., Wick, W., Hamprecht, F. A., Bendszus, M., and Biller, A., “Virtual Raters for Reproducible and Objective Assessments in Radiology”, Nature Scientific Reports, vol. 6, 2016.PDF icon Technical Report (2.81 MB)
M. Hanselmann, Köthe, U., Kirchner, M., Renard, B. Y., Amstalden, E. R., Glunde, K., Heeren, R. M. A., and Hamprecht, F. A., “Towards Digital Staining using Imaging Mass Spectrometry and Random Forests”, Journal of Proteome Research, vol. 8, pp. 3558-3567, 2009.PDF icon Technical Report (1.47 MB)
H. Meine, Köthe, U., and Stelldinger, P., “A Topological Sampling Theorem for Robust Boundary Reconstruction and Image Segmentation”, Discrete Applied Mathematics, vol. 157, pp. 524-541, 2008.
M. Frank, Plaue, M., Rapp, H., Köthe, U., Jähne, B., and Hamprecht, F. A., “Theoretical and Experimental Error Analysis of Continuous-Wave Time-Of-Flight Range Cameras”, Optical Engineering, vol. 48, 013602, 2009.PDF icon Technical Report (2.03 MB)
M. Frank, Plaue, M., Rapp, H., Köthe, U., Jähne, B., and Hamprecht, F. A., “Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras”, Opt. Eng., vol. 48, p. 013602, 2009.
U. Köthe, Herrmannsdörfer, F., Kats, I., and Hamprecht, F. A., “SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy”, Histochemistry and Cell Biology, vol. 141, pp. 613-627, 2014.PDF icon Technical Report (2.29 MB)
M. Hanselmann, Voss, B., Renard, B. Y., Lindner, M., Köthe, U., Kirchner, M., and Hamprecht, F. A., “SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists”, Bioinformatics, vol. 27 (7), pp. 987-993, 2011.PDF icon Technical Report (2.2 MB)
B. Andres, Köthe, U., Kröger, T., and Hamprecht, F. A., “Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x”, ArXiv e-prints, 2010.PDF icon Technical Report (415.54 KB)
X. Lou, Fiaschi, L., Köthe, U., and Hamprecht, F. A., “Quality Classification of Microscopic Imagery with Weakly Supervised Learning”, MICCAI-MLMI. Proceedings, pp. 176-183, 2012.PDF icon Technical Report (4.15 MB)
N. Krasowki, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Neuron Segmentation with High-Level Biological Priors”, IEEE Transactions on Medical Imaging, vol. 37, no. 4, 2017.
T. Beier, Pape, C., Rahaman, N., Prange, T., Berg, S., Bock, D., Cardona, A., Knott, G. W., Plaza, S. M., Scheffer, L. K., Köthe, U., Kreshuk, A., and Hamprecht, F. A., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 2017.
L. Fiaschi, Nair, R., Köthe, U., and Hamprecht, F. A., “Learning to Count with Regression Forest and Structured Labels”, ICPR 2012. Proceedings, pp. 2685-2688, 2012.PDF icon Technical Report (3.66 MB)
B. Andres, Kappes, J. H., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search”, ArXiv e-prints, 2010.PDF icon Technical Report (625.06 KB)
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “ilastik: interactive machine learning for (bio)image analysis”, Nature Methods, vol. 16, pp. 1226-1232, 2019.
B. Andres, Köthe, U., Kröger, T., and Hamprecht, F. A., “How to Extract the Geometry and Topology from Very Large 3D Segmentations”, ArXiv e-prints, 2010.PDF icon Technical Report (1.44 MB)
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
M. Schiegg, Hanslovsky, P., Haubold, C., Köthe, U., Hufnagel, L., and Hamprecht, F. A., “Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell”, Bioinformatics, vol. 31, no. 6, pp. 948-956, 2015.PDF icon Technical Report (534.29 KB)
L. Ardizzone, Mackowiak, R., Rother, C., and Köthe, U., “Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling”, 2020.PDF icon PDF (2.87 MB)
B. Andres, Kondermann, C., Kondermann, D., Köthe, U., Hamprecht, F. A., and Garbe, C. S., “On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation”, Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1-6, 2008.PDF icon Technical Report (1.58 MB)
X. Lou, Kirchner, M., Renard, B. Y., Köthe, U., Graf, C., Lee, C., Steen, J. A. J., Steen, H., Mayer, M. P., and Hamprecht, F. A., “Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments”, Bioinformatics, vol. 26(12), pp. 1535-1541, 2010.PDF icon Technical Report (518.01 KB)
M. Kirchner, Renard, B. Y., Köthe, U., Pappin, D. J., Hamprecht, F. A., Steen, J. A. J., and Steen, H., “Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments”, Bioinformatics, vol. 26 (1), pp. 77-83, 2010.PDF icon Technical Report (380.19 KB)
A. Kreshuk, Walecki, R., Köthe, U., Gierthmühlen, M., Plachta, D., Genoud, C., Haastert-Talini, K., and Hamprecht, F. A., “Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves”, Journal of Microscopy, vol. 259 (2), pp. 143-154, 2015.
A. Kreshuk, Köthe, U., Pax, E., Bock, D. D., and Hamprecht, F. A., “Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks”, PLoS ONE, vol. 9, p. 2, 2014.PDF icon Technical Report (16.66 MB)
A. Kreshuk, Straehle, C. N., Sommer, C., Köthe, U., Cantoni, M., Knott, G. W., and Hamprecht, F. A., “Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images”, PLoS ONE, vol. 6 (10), 2011.PDF icon Technical Report (290.48 KB)
M. Hanselmann, Röder, J., Köthe, U., Renard, B. Y., Heeren, R. M. A., and Hamprecht, F. A., “Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images”, Analytical Chemistry, vol. 85 (1), pp. 147-155, 2012.PDF icon Technical Report (2.58 MB)
B. Andres, Köthe, U., Kröger, T., Helmstaedter, M., Briggmann, K. L., Denk, W., and Hamprecht, F. A., “3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries”, Medical Image Analysis, vol. 16 (2012), pp. 796-805, 2012.PDF icon Technical Report (20.85 MB)
inproceedings
S. Wolf, Schott, L., Köthe, U., and Hamprecht, F. A., “Learned Watershed: End-to-End Learning of Seeded Segmentation”, ICCV. pp. 2030-2038, 2017.PDF icon Technical Report (3.76 MB)
In Collection
U. Köthe, “What Can We Learn from Discrete Images about the Continuous World”, Discrete Geometry for Computer Imagery, vol. 4992. Springer, pp. 4-19, 2008.
Conference Proceedings
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, ECCV. Proceedings. Springer, pp. 571-587, 2018.
T. Beier, Andres, B., Köthe, U., and Hamprecht, F. A., “An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”, ECCV. Proceedings, vol. LNCS 9906. Springer, pp. 715-730, 2016.PDF icon Technical Report (4.89 MB)
Conference Paper
C. N. Straehle, Köthe, U., and Hamprecht, F. A., “Weakly supervised learning of image partitioning using decision trees with structured split criteria”, in ICCV 2013. Proceedings, 2013, pp. 1849-1856.PDF icon Technical Report (5.97 MB)
L. Fiaschi, Diego, F., Grosser, K. - H., Schiegg, M., Köthe, U., Zlatic, M., and Hamprecht, F. A., “Tracking indistinguishable translucent objects over time using weakly supervised structured learning”, in CVPR. Proceedings, 2014, pp. 2736 - 2743.PDF icon Technical Report (1.47 MB)

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