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2012

B. Andres, Kröger, T., Briggmann, K. L., Denk, W., Norogod, N., Knott, G. W., Köthe, U., and Hamprecht, F. A., “Globally Optimal Closed-Surface Segmentation for Connectomics”, in ECCV 2012. Proceedings, Part 3, 2012, pp. 778-791.

Technical Report (2.72 MB)

B. Andres, Kappes, J. Hendrik, Beier, T., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models”, in ECCV 2012, 2012.

B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models”, in ECCV 2012, 2012.

Technical Report (532.64 KB)

B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models”, in Computer Vision - {ECCV} 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part {VII}, 2012.

Technical Report (446.28 KB)

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.

Technical Report (3.66 MB)

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.

Technical Report (4.15 MB)

C. N. Straehle, Köthe, U., Briggman, K., Denk, W., and Hamprecht, F. A., “Seeded watershed cut uncertainty estimators for guided interactive segmentation”, in CVPR 2012. Proceedings, 2012, pp. 765 - 772.

Technical Report (2.84 MB)

2013

C. N. Straehle, Peter, S., Köthe, U., and Hamprecht, F. A., “K-smallest Spanning Tree Segmentations”, in German Conference on Pattern Recognition (DAGM/GCPR). Proceedings, 2013, pp. 375-384.

Technical Report (1.18 MB)

T. Kröger, Mikula, S., Denk, W., Köthe, U., and Hamprecht, F. A., “Learning to Segment Neurons with Non-local Quality Measures”, in MICCAI 2013. Proceedings, part II, 2013, vol. 8150, pp. 419-427.

Technical Report (2.87 MB)

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.

Technical Report (5.97 MB)

2014

T. Kröger, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Asymmetric Cuts: Joint Image Labeling and Partitioning”, in Pattern Recognition - 36th German Conference, {GCPR} 2014, Münster, Germany, September 2-5, 2014, Proceedings, 2014.

Technical Report (3.46 MB)

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.

Technical Report (16.66 MB)

T. Beier, Kröger, T., Kappes, J. H., Köthe, U., and Hamprecht, F. A., “Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning”, in 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014, 2014.

Technical Report (10.06 MB)

J. Kleesiek, Biller, A., Urban, G., Köthe, U., Bendszus, M., and Hamprecht, F. A., “ilastik for Multi-modal Brain Tumor Segmentation”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace, 2014, pp. 12-17.

Technical Report (405.91 KB)

C. N. Straehle, Kandemir, M., Köthe, U., and Hamprecht, F. A., “Multiple instance learning with response-optimized random forests”, in ICPR. Proceedings, 2014, pp. 3768 - 3773.

Technical Report (296.66 KB)

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.

Technical Report (2.29 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.

Technical Report (1.47 MB)

2015

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.

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.

Technical Report (534.29 KB)

N. Krasowski, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors”, in 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015, 2015, pp. 536-539.

Technical Report (2.25 MB)

M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.

Technical Report (648.55 KB)

2016

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.

Technical Report (4.89 MB)

C. Haubold, Schiegg, M., Kreshuk, A., Berg, S., Köthe, U., and Hamprecht, F. A., “Segmenting and Tracking Multiple Dividing Targets Using ilastik”, in Focus on Bio-Image Informatics, vol. 219, Springer, 2016, pp. 199-229.

Technical Report (4.46 MB)

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.

Technical Report (2.81 MB)

2017

S. Wolf, Schott, L., Köthe, U., and Hamprecht, F. A., “Learned Watershed: End-to-End Learning of Seeded Segmentation”, ICCV. pp. 2030-2038, 2017.

Technical Report (3.76 MB)

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.

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.

2018

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.

S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 571–587.

2019

J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.

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.

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.

T. J. Adler, Ayala, L., Ardizzone, L., Kenngott, H. G., Vemuri, A., Müller-Stich, B. P., Rother, C., Köthe, U., and Maier-Hein, L., “Out of Distribution Detection for Intra-operative Functional Imaging”, in MICCAI UNSURE Workshop 2019, 2019, vol. 11840 LNCS, pp. 75–82.

PDF (3.1 MB)

2020

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 (2.87 MB)

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