@article {Ardizzone2020,
	title = {Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling},
	year = {2020},
	month = {jan},
	abstract = {Generative models are more informative about underlying phenomena than discriminative ones and offer superior uncertainty quantification and out-of-distribution robustness. However, these advantages often come at the expense of reduced classification accuracy. The Information Bottleneck objective (IB) formulates this trade-off in a clean information-theoretic way, but its practical application is hampered by a lack of accurate high-dimensional estimators of mutual information (MI), its main constituent. To overcome this limitation, we develop the theory and methodology of IB-INNs, which optimize the IB objective by means of Invertible Neural Networks (INNs), without the need for approximations of MI. Our experiments show that IB-INNs allow for a precise adjustment of the generative/discriminative trade-off: They learn accurate models of the class conditional likelihoods, generalize well to unseen data and reliably detect out-of-distribution examples, while at the same time exhibiting classification accuracy close to purely discriminative feed-forward networks.},
	url = {http://arxiv.org/abs/2001.06448},
	author = {Lynton Ardizzone and Mackowiak, Radek and Carsten Rother and Ullrich K{\"o}the}
}
@techreport {Kruse2019,
	title = {Benchmarking Invertible Architectures on Inverse Problems},
	number = {i},
	year = {2019},
	abstract = {Recent work demonstrated that flow-based invert-ible neural networks are promising tools for solving ambiguous inverse problems. Following up on this, we investigate how ten invertible archi-tectures and related models fare on two intuitive, low-dimensional benchmark problems, obtaining the best results with coupling layers and simple autoencoders. We hope that our initial efforts inspire other researchers to evaluate their invertible architectures in the same setting and put forth additional benchmarks, so our evaluation may eventually grow into an official community challenge.},
	author = {Kruse, Jakob and Lynton Ardizzone and Carsten Rother and Ullrich K{\"o}the}
}
@article {Ardizzone2019,
	title = {Guided Image Generation with Conditional Invertible Neural Networks},
	year = {2019},
	abstract = {In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.},
	url = {http://arxiv.org/abs/1907.02392},
	author = {Lynton Ardizzone and Carsten L{\"u}th and Kruse, Jakob and Carsten Rother and Ullrich K{\"o}the}
}
@article {Ardizzone2019a,
	title = {Guided Image Generation with Conditional Invertible Neural Networks},
	year = {2019},
	abstract = {In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.},
	url = {http://arxiv.org/abs/1907.02392},
	author = {Lynton Ardizzone and Carsten L{\"u}th and Kruse, Jakob and Carsten Rother and Ullrich K{\"o}the}
}
@article {6334,
	title = {ilastik: interactive machine learning for (bio)image analysis},
	journal = {Nature Methods},
	volume = {16},
	year = {2019},
	pages = {1226-1232},
	doi = {10.1038/s41592-019-0582-9},
	author = {Stuart Berg and Kutra, D and Kroeger, T and Christoph N. Straehle and Bernhard X. Kausler and Haubold, C. and Schiegg, M and Ales, J and Thorsten Beier and Rudy, M and Eren, K and Cervantes, JI and Xu, B and Beuttenm{\"u}ller, F and Wolny, A and Zhang, C and Ullrich K{\"o}the and Fred A. Hamprecht and Kreshuk, A}
}
@conference {Adler2019,
	title = {Out of Distribution Detection for Intra-operative Functional Imaging},
	booktitle = {MICCAI UNSURE Workshop 2019},
	volume = {11840 LNCS},
	year = {2019},
	pages = {75{\textendash}82},
	abstract = {Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.},
	issn = {16113349},
	doi = {10.1007/978-3-030-32689-0_8},
	author = {Adler, Tim J and Ayala, Leonardo and Lynton Ardizzone and Kenngott, Hannes G and Vemuri, Anant and M{\"u}ller-Stich, Beat P and Carsten Rother and Ullrich K{\"o}the and Maier-Hein, Lena}
}
@proceedings {6275,
	title = {The Mutex Watershed: Efficient, Parameter-Free Image   Partitioning},
	year = {2018},
	pages = {571-587},
	publisher = {Springer},
	doi = {10.1007/978-3-030-01225-0_34},
	author = {Wolf, S and Pape, C and Bailoni, A and Rahaman, N and Kreshuk, A. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Wolf2018,
	title = {The Mutex Watershed: Efficient, Parameter-Free Image Partitioning},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11208 LNCS},
	year = {2018},
	month = {apr},
	pages = {571{\textendash}587},
	abstract = {Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments; or equivalently, the task of detecting closed contours in an image. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as an NP-hard signed graph partitioning problem. Here, we propose an algorithm with empirically linearithmic complexity. Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. The algorithm itself, which we dub {\textquotedblleft}Mutex Watershed{\textquotedblright}, is closely related to a minimal spanning tree computation. It is deterministic and easy to implement. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives results that currently define the state-of-the-art in the competitive ISBI 2012 EM segmentation benchmark. These results are also better than those obtained from other recently proposed clustering strategies operating on the very same network outputs.},
	isbn = {9783030012243},
	issn = {16113349},
	doi = {10.1007/978-3-030-01225-0_34},
	url = {http://arxiv.org/abs/1904.12654},
	author = {Wolf, Steffen and Pape, Constantin and Bailoni, Alberto and Rahaman, Nasim and Kreshuk, Anna and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {6212,
	title = {Learned Watershed: End-to-End Learning of Seeded Segmentation},
	year = {2017},
	pages = {2030-2038},
	author = {Wolf, S and Schott, L and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {6122,
	title = {Multicut brings automated neurite segmentation closer to human performance},
	journal = {Nature Methods},
	volume = {14},
	year = {2017},
	pages = {101-102},
	doi = {10.1038/nmeth.4151},
	url = {http://rdcu.be/oVDQ},
	author = {Thorsten Beier and Pape, C and Rahaman, N and Prange, T and Stuart Berg and Bock, D and A. Cardona and G. W. Knott and Plaza, S M and Scheffer, L K and Ullrich K{\"o}the and Kreshuk, A and Fred A. Hamprecht}
}
@article {6178,
	title = {Neuron Segmentation with High-Level Biological Priors},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2017},
	chapter = {829-839},
	doi = {10.1109/TMI.2017.2712360},
	author = {Krasowki, N and Thorsten Beier and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht and Kreshuk, A.}
}
@proceedings {6078,
	title = {An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem},
	volume = {LNCS 9906},
	year = {2016},
	pages = {715-730},
	publisher = {Springer},
	doi = { 10.1007/978-3-319-46475-6_44},
	author = {Thorsten Beier and Bj{\"o}rn Andres and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@inbook {6055,
	title = {Segmenting and Tracking Multiple Dividing Targets Using ilastik},
	booktitle = {Focus on Bio-Image Informatics},
	series = {Advances in Anatomy, Embryology and Cell Biology},
	volume = {219},
	year = {2016},
	pages = {199-229},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-28549-8_8},
	author = {Haubold, C. and Schiegg, M. and Kreshuk, A. and Stuart Berg and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {6033,
	title = {Virtual Raters for Reproducible and Objective Assessments in Radiology},
	journal = {Nature Scientific Reports},
	volume = {6},
	year = {2016},
	doi = {10.1038/srep25007},
	author = {Kleesiek, J. and Petersen, J. and D{\"o}ring, M. and Maier-Hein, K. and Ullrich K{\"o}the and Wick, W. and Fred A. Hamprecht and M. Bendszus and A. Biller}
}
@article {kreshuk_15_automated,
	title = {Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves},
	journal = {Journal of Microscopy},
	volume = {259 (2)},
	year = {2015},
	pages = {143-154},
	doi = {10.1111/jmi.12266},
	author = {Anna Kreshuk and Walecki, R. and Ullrich K{\"o}the and Gierthm{\"u}hlen, M. and Plachta, D. and Genoud, C. and Haastert-Talini, K. and Fred A. Hamprecht}
}
@article {schiegg_15_graphical,
	title = {Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell},
	journal = {Bioinformatics},
	volume = {31},
	number = {6},
	year = {2015},
	note = {1},
	pages = {948-956},
	doi = {10.1093/bioinformatics/btu764},
	url = {http://bioinformatics.oxfordjournals.org/content/early/2014/11/17/bioinformatics.btu764.full.pdf?keytype=ref\&ijkey=mTXWsiFrci7R8tc},
	author = {Schiegg, M. and Hanslovsky, P. and Haubold, C. and Ullrich K{\"o}the and Hufnagel, L. and Fred A. Hamprecht}
}
@conference {krasowski_15_improving,
	title = {Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors},
	booktitle = {12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015},
	year = {2015},
	note = {1},
	pages = {536-539},
	doi = {10.1109/ISBI.2015.7163929},
	author = {Niko Krasowski and Thorsten Beier and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht and Anna Kreshuk}
}
@conference {schiegg_15_proof-reading,
	title = {Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models},
	booktitle = {ISBI. Proceedings},
	year = {2015},
	note = {1},
	pages = {394-398},
	doi = {10.1109/ISBI.2015.7163895},
	author = {Schiegg, M. and Heuer, B. and Haubold, C. and Wolf, S. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {kroeger_14_asymmetric,
	title = {Asymmetric Cuts: Joint Image Labeling and Partitioning},
	booktitle = {Pattern Recognition - 36th German Conference, {GCPR} 2014, M{\"u}nster, Germany, September 2-5, 2014, Proceedings},
	year = {2014},
	doi = {10.1007/978-3-319-11752-2_16},
	url = {http://dx.doi.org/10.1007/978-3-319-11752-2_16},
	author = {Thorben Kr{\"o}ger and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {kreshuk_14_automated,
	title = {Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks},
	journal = {PLoS ONE},
	volume = {9},
	year = {2014},
	note = {1},
	pages = {2},
	doi = {10.1371/journal.pone.0087351},
	author = {Anna Kreshuk and Ullrich K{\"o}the and Pax, E. and Bock, D. D. and Fred A. Hamprecht}
}
@conference {beier_14_cut,
	title = {Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning},
	booktitle = {2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014},
	year = {2014},
	doi = {10.1109/CVPR.2014.17},
	url = {http://dx.doi.org/10.1109/CVPR.2014.17},
	author = {Thorsten Beier and Thorben Kr{\"o}ger and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {kleesiek_14_ilastik,
	title = {ilastik for Multi-modal Brain Tumor Segmentation},
	booktitle = {MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace},
	year = {2014},
	pages = {12-17},
	author = {Kleesiek, J. and A. Biller and Urban, G. and Ullrich K{\"o}the and M. Bendszus and Fred A. Hamprecht}
}
@conference {straehle_14_multiple,
	title = {Multiple instance learning with response-optimized random forests},
	booktitle = {ICPR. Proceedings},
	year = {2014},
	note = {1},
	pages = {3768 - 3773},
	doi = {10.1109/ICPR.2014.647},
	author = {Christoph N. Straehle and Kandemir, M. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {koethe_14_simplestorm,
	title = {SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy},
	journal = {Histochemistry and Cell Biology},
	volume = {141},
	year = {2014},
	note = {1},
	pages = {613-627},
	doi = {10.1007/s00418-014-1211-4},
	author = {Ullrich K{\"o}the and Herrmannsd{\"o}rfer, F. and Kats, I. and Fred A. Hamprecht}
}
@conference {fiaschi_14_tracking,
	title = {Tracking indistinguishable translucent objects over time using weakly supervised structured learning},
	booktitle = {CVPR. Proceedings},
	year = {2014},
	note = {1},
	pages = {2736 - 2743},
	doi = {10.1109/CVPR.2014.356},
	author = {Fiaschi, L. and Ferran Diego and Karl-Heinz Grosser and Schiegg, M. and Ullrich K{\"o}the and Zlatic, M. and Fred A. Hamprecht}
}
@conference {straehle_13_ksmallest,
	title = {K-smallest Spanning Tree Segmentations},
	booktitle = {German Conference on Pattern Recognition (DAGM/GCPR). Proceedings},
	number = {8142},
	year = {2013},
	note = {1},
	pages = {375-384},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-40602-7_40},
	author = {Christoph N. Straehle and Peter, S. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {kroeger_13_learning,
	title = {Learning to Segment Neurons with Non-local Quality Measures},
	booktitle = {MICCAI 2013. Proceedings, part II},
	volume = {8150},
	year = {2013},
	note = {1},
	pages = {419-427},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-40763-5_52},
	author = {Thorben Kr{\"o}ger and Mikula, S. and Denk, W. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {straehle_13_weakly,
	title = {Weakly supervised learning of image partitioning using decision trees with structured split criteria},
	booktitle = {ICCV 2013. Proceedings},
	year = {2013},
	note = {1},
	pages = {1849-1856},
	doi = {10.1109/ICCV.2013.232},
	author = {Christoph N. Straehle and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {andres_11_3d,
	title = {3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries},
	journal = {Medical Image Analysis},
	volume = {16 (2012)},
	year = {2012},
	note = {1},
	pages = {796-805},
	doi = {10.1016/j.media.2011.11.004},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Thorben Kr{\"o}ger and Helmstaedter, M. and K. L. Briggmann and Denk, W. and Fred A. Hamprecht}
}
@article {hanselmann_12_active,
	title = {Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images},
	journal = {Analytical Chemistry},
	volume = {85 (1)},
	year = {2012},
	note = {1},
	pages = {147-155},
	doi = {10.1021/ac3023313},
	author = {Hanselmann, M. and R{\"o}der, J. and Ullrich K{\"o}the and B. Y. Renard and Heeren, R. M. A. and Fred A. Hamprecht}
}
@conference {kausler_12_discrete,
	title = {A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness},
	booktitle = {ECCV 2012. Proceedings},
	volume = {7574},
	year = {2012},
	note = {1},
	pages = {144-157},
	doi = {10.1007/978-3-642-33712-3_11},
	author = {Bernhard X. Kausler and Schiegg, M. and Bj{\"o}rn Andres and Lindner, M. and Ullrich K{\"o}the and Leitte, H. and Wittbrodt, J. and Hufnagel, L. and Fred A. Hamprecht}
}
@conference {andres_12_globally,
	title = {Globally Optimal Closed-Surface Segmentation for Connectomics},
	booktitle = {ECCV 2012. Proceedings, Part 3},
	number = {7574},
	year = {2012},
	note = {1},
	pages = {778-791},
	doi = {10.1007/978-3-642-33712-3_56},
	author = {Bj{\"o}rn Andres and Thorben Kr{\"o}ger and K. L. Briggmann and Denk, W. and Norogod, N. and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres12,
	title = {The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models},
	booktitle = {ECCV 2012},
	year = {2012},
	author = {Bj{\"o}rn Andres and J{\"o}rg Hendrik Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {andres_12_lazy,
	title = {The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models},
	booktitle = {Computer Vision - {ECCV} 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part {VII}},
	year = {2012},
	doi = {10.1007/978-3-642-33786-4_12},
	url = {http://dx.doi.org/10.1007/978-3-642-33786-4_12},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres12,
	title = {The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models},
	booktitle = {ECCV 2012},
	year = {2012},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {fiaschi_12_learning,
	title = {Learning to Count with Regression Forest and Structured Labels},
	journal = {ICPR 2012. Proceedings},
	year = {2012},
	note = {1},
	pages = {2685-2688},
	author = {Fiaschi, L. and Nair, R. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {lou_12_quality,
	title = {Quality Classification of Microscopic Imagery with Weakly Supervised Learning},
	journal = {MICCAI-MLMI. Proceedings},
	year = {2012},
	note = {1},
	pages = {176-183},
	doi = {10.1007/978-3-642-35428-1_22},
	author = {Lou, X. and Fiaschi, L. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {straehle_12_seeded,
	title = {Seeded watershed cut uncertainty estimators for guided interactive segmentation},
	booktitle = {CVPR 2012. Proceedings},
	year = {2012},
	note = {1},
	pages = {765 - 772},
	doi = {10.1109/CVPR.2012.6247747},
	author = {Christoph N. Straehle and Ullrich K{\"o}the and Briggman, K. and Denk, W. and Fred A. Hamprecht}
}
@article {kreshuk_11_automated2,
	title = {Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images},
	journal = {PLoS ONE},
	volume = {6 (10)},
	year = {2011},
	doi = {10.1371/journal.pone.0024899},
	author = {Anna Kreshuk and Christoph N. Straehle and Christoph Sommer and Ullrich K{\"o}the and Cantoni, M. and G. W. Knott and Fred A. Hamprecht}
}
@conference {kreshuk_11_automated,
	title = {Automated Segmentation of Synapses in 3D EM Data},
	booktitle = {Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011).
Proceedings},
	year = {2011},
	note = {1},
	pages = {220-223},
	doi = {10.1109/ISBI.2011.5872392},
	author = {Anna Kreshuk and Christoph N. Straehle and Christoph Sommer and Ullrich K{\"o}the and G. W. Knott and Fred A. Hamprecht}
}
@conference {straehle_11_carving,
	title = {Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images},
	booktitle = {MICCAI 2011, Proceedings.},
	volume = {6891},
	year = {2011},
	note = {1},
	pages = {653-660},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23623-5_82},
	author = {Christoph N. Straehle and Ullrich K{\"o}the and G. W. Knott and Fred A. Hamprecht}
}
@conference {lou_11_deltr,
	title = {DELTR: Digital Embryo Lineage Tree Reconstructor},
	booktitle = {Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings},
	year = {2011},
	note = {1},
	pages = {1557-1560},
	doi = {10.1109/ISBI.2011.5872698},
	author = {Lou, X. and F. O. Kaster and Lindner, M. and Bernhard X. Kausler and Ullrich K{\"o}the and H{\"o}ckendorf, B. and Wittbrodt, J. and J{\"a}nicke, H. and Fred A. Hamprecht}
}
@conference {sommer_11_ilastik,
	title = {ilastik: Interactive Learning and Segmentation Toolkit},
	booktitle = {Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011).Proceedings},
	year = {2011},
	note = {1},
	pages = {230-233},
	doi = {10.1109/ISBI.2011.5872394},
	author = {Christoph Sommer and Christoph N. Straehle and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {menze_11_on,
	title = {On oblique random forests},
	booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings.},
	year = {2011},
	note = {1},
	pages = {453-469},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23783-6_29},
	author = {Bjoern H. Menze and B. Michael Kelm and Splitthoff, N. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres11,
	title = {Probabilistic Image Segmentation with Closedness Constraints},
	booktitle = {Proceedings of ICCV},
	year = {2011},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres11,
	title = {Probabilistic Image Segmentation with Closedness Constraints},
	booktitle = {Proceedings of ICCV},
	year = {2011},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {andres_11_probabilistic,
	title = {Probabilistic Image Segmentation with Closedness Constraints},
	booktitle = {ICCV, Proceedings},
	year = {2011},
	note = {1},
	pages = {2611 - 2618},
	doi = {10.1109/ICCV.2011.6126550},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {hanselmann_11_sima,
	title = {SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists},
	journal = {Bioinformatics},
	volume = {27 (7)},
	year = {2011},
	note = {1},
	pages = {987-993},
	doi = {10.1093/bioinformatics/btr051},
	author = {Hanselmann, M. and Bj{\"o}rn Voss and B. Y. Renard and Lindner, M. and Ullrich K{\"o}the and Kirchner, M. and Fred A. Hamprecht}
}
@article {kirchner_10_computational,
	title = {Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments},
	journal = {Bioinformatics},
	volume = {26 (1)},
	year = {2010},
	note = {1},
	pages = {77-83},
	doi = {10.1093/bioinformatics/btp607},
	author = {Kirchner, M. and B. Y. Renard and Ullrich K{\"o}the and Pappin, D. J. and Fred A. Hamprecht and Judith A. J. Steen and Steen, H.}
}
@article {lou_10_deuteration,
	title = {Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments},
	journal = {Bioinformatics},
	volume = {26(12)},
	year = {2010},
	note = {1},
	pages = {1535-1541},
	doi = {10.1093/bioinformatics/btq165},
	author = {Lou, X. and Kirchner, M. and B. Y. Renard and Ullrich K{\"o}the and Graf, C. and Lee, C. and Judith A. J. Steen and Steen, H. and Mayer, M. P. and Fred A. Hamprecht}
}
@conference {Kappes-DAGM2010,
	title = {An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM},
	booktitle = {Pattern Recognition, Proc.~32th DAGM Symposium},
	year = {2010},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Christoph Schn{\"o}rr and Fred A. Hamprecht}
}
@conference {andres_10_empirical,
	title = {An Empirical Comparison of Inference Algorithms for Graphical Models
with Higher Order Factors Using OpenGM},
	booktitle = {Pattern Recognition, Proc.~32th DAGM Symposium},
	number = {6376},
	year = {2010},
	note = {1},
	pages = {353-362},
	doi = {10.1007/978-3-642-15986-2_36},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Christoph Schn{\"o}rr and Fred A. Hamprecht}
}
@conference {koethe_10_geometric,
	title = {Geometric Analysis of 3D Electron Microscopy Data},
	booktitle = {Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	note = {1},
	pages = {22-26},
	author = {Ullrich K{\"o}the and Bj{\"o}rn Andres and Thorben Kr{\"o}ger and Fred A. Hamprecht},
	editor = {Ullrich K{\"o}the and Montanvert, A. and Soille, P.}
}
@article {andres_10_how,
	title = {How to Extract the Geometry and Topology from Very Large 3D Segmentations},
	journal = {ArXiv e-prints},
	year = {2010},
	note = {1},
	url = {http://arxiv.org/abs/1009.6215},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Thorben Kr{\"o}ger and Fred A. Hamprecht}
}
@article {andres_10_lazy,
	title = {The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search},
	journal = {ArXiv e-prints},
	year = {2010},
	note = {1},
	url = {http://arxiv.org/abs/1009.4102},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {andres_10_runtime,
	title = {Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x},
	journal = {ArXiv e-prints},
	year = {2010},
	note = {1},
	url = {http://arxiv.org/abs/1008.2909v1},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Thorben Kr{\"o}ger and Fred A. Hamprecht}
}
@conference {baehnisch_09_fast,
	title = {Fast and Accurate 3D Edge Detection for Surface Reconstruction},
	booktitle = {Pattern Recognition},
	volume = {5748},
	year = {2009},
	pages = {111-120},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03798-6},
	author = {B{\"a}hnisch, C. and Stelldinger, P. and Ullrich K{\"o}the}
}
@conference {hanselmann_09_multivariate,
	title = {Multivariate Watershed Segmentation of Compositional Data},
	booktitle = {Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press},
	volume = {5810},
	year = {2009},
	note = {1},
	pages = {180-192},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Hanselmann, M. and Ullrich K{\"o}the and B. Y. Renard and Kirchner, M. and Heeren, R. M. A. and Fred A. Hamprecht}
}
@conference {meine_09_topological,
	title = {Pixel Approximation Errors in Common Watershed Algorithms},
	booktitle = {Discrete Geometry for Computer Imagery},
	volume = {5810},
	year = {2009},
	pages = {193-202},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@conference {meine_09_pixel,
	title = {Pixel Approximation Errors in Common Watershed Algorithms},
	booktitle = {Discrete Geometry for Computer Imagery},
	volume = {5810},
	year = {2009},
	note = {1},
	pages = {193-202},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@conference {andres_09_quantitative,
	title = {Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times},
	booktitle = {Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings},
	volume = {5748},
	year = {2009},
	note = {1},
	pages = {502-511},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03798-6},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Bonea, A. and Nadler, B. and Fred A. Hamprecht}
}
@article {frank2009,
	title = {Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras},
	journal = {Opt. Eng.},
	volume = {48},
	year = {2009},
	pages = {013602},
	abstract = {We offer a formal investigation of the measurement principle of time-of-flight 3-D cameras using correlation of amplitude-modulated continuous-wave signals. These sensors can provide both depth maps and IR intensity pictures simultaneously and in real time. We examine the theory of the data acquisition in detail. The variance of the range measurements is derived in a concise way and we show that the computed range follows an offset normal distribution. The impact of quantization of that distribution is discussed. All theoretically investigated errors like the behavior of the variance, depth bias, saturation and quantization effects are supported by experimental results.},
	doi = {10.1117/1.3070634},
	author = {Mario Frank and Matthias Plaue and Holger Rapp and Ullrich K{\"o}the and Bernd J{\"a}hne and Fred A. Hamprecht}
}
@article {frank_09_theoretical,
	title = {Theoretical and Experimental Error Analysis of Continuous-Wave Time-Of-Flight Range Cameras},
	journal = {Optical Engineering},
	volume = {48, 013602},
	year = {2009},
	note = {1},
	doi = {10.1117/1.3070634},
	author = {Mario Frank and Matthias Plaue and Holger Rapp and Ullrich K{\"o}the and Bernd J{\"a}hne and Fred A. Hamprecht}
}
@article {hanselmann_09_towards,
	title = {Towards Digital Staining using Imaging Mass Spectrometry and Random Forests},
	journal = {Journal of Proteome Research},
	volume = {8},
	year = {2009},
	note = {1},
	pages = {3558-3567},
	doi = {10.1021/pr900253y},
	author = {Hanselmann, M. and Ullrich K{\"o}the and Kirchner, M. and B. Y. Renard and Amstalden, E. R. and Glunde, K. and Heeren, R. M. A. and Fred A. Hamprecht}
}
@article {andres_08_errors-in-variables,
	title = {On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation},
	journal = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	note = {1},
	pages = {1-6},
	doi = {10.1109/CVPR.2008.4587571},
	author = {Bj{\"o}rn Andres and Claudia Kondermann and Daniel Kondermann and Ullrich K{\"o}the and Fred A. Hamprecht and Christoph S. Garbe}
}
@article {koethe_08_reliable-image-analysis,
	title = {Reliable Low-Level Image Analysis},
	year = {2008},
	publisher = {Department Informatik, University of Hamburg},
	address = {Hamburg},
	abstract = {What information give <em>discrete images</em> about the <em>continuous world</em>?

Image analysis uses discrete methods to make statements about the continuous real world. Since an in
finite amount of information is lost by digitization, it is not obviuous whether or when this approa
ch will succeed: Can one prove that certain properties of interest will be preserved, despite the in
formation loss?

This habilitation thesis considers theories which explicitly connect continuous and discrete models,
 such as Shannon{\textquoteright}s famous sampling theorem and a recently discovered geometric sampling theorem. Thi
s analysis reveals important consequences regarding the necessary image quality (e.g. resolution and
 signal-to-noise-ratio) and the resulting limits of observation. These findings are subsequently app
lied to a large number of low-level image analysis problems (such edge and corner detection, segment
ation, local estimation, and noise normalization), which leads to significantly improved algorithms
that perform robustly and accurately in accordance to the predictions of theory.</div>},
	author = {Ullrich K{\"o}the}
}
@conference {andres_08_segmentation,
	title = {Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification},
	booktitle = {Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings},
	volume = {5096},
	year = {2008},
	note = {1},
	pages = {142-152},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-69321-5_15},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Helmstaedter, M. and Denk, W. and Fred A. Hamprecht},
	editor = {Gerhard Rigoll}
}
@article {meine_08_topological,
	title = {A Topological Sampling Theorem for Robust Boundary Reconstruction
and Image Segmentation},
	journal = {Discrete Applied Mathematics},
	volume = {157},
	number = {3},
	year = {2008},
	pages = {524-541},
	doi = {10.1016/j.dam.2008.05.031},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@incollection {koethe_08_what,
	title = {What Can We Learn from Discrete Images about the Continuous World},
	volume = {4992},
	year = {2008},
	pages = {4-19},
	publisher = {Springer},
	doi = {10.1007/978-3-540-79126-3_2},
	author = {Ullrich K{\"o}the}
}
