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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.

Technical Report (1.44 MB)

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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. Sommer, Straehle, C. N., Köthe, U., and Hamprecht, F. A., “ilastik: Interactive Learning and Segmentation Toolkit”, in Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011).Proceedings, 2011, pp. 230-233.

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.

M. Frank and Hamprecht, F. A., “Image-Based Supervision of a Periodically Working Machine”, Pattern Analysis and Applications, pp. 1-10, 2011.

Technical Report (466.61 KB)

E. Meijering, Carpenter, A. E., Peng, H., Hamprecht, F. A., and Olivo-Marin, J., “Imagining the future of bioimage analysis”, Nature Biotechnology, vol. 34, no. 12, pp. 1250-1255, 2016.

Technical Report (924.57 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. Kandemir and Hamprecht, F. A., “Instance Label Prediction by Dirichlet Process Multiple Instance Learning”, in UAI. Proceedings, 2014.

Technical Report (4.26 MB)

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L. Fiaschi, Grosser, K. - H., Afonso, B., Zlatic, M., and Hamprecht, F. A., “Keeping Count: Leveraging Temporal Context to Count Heavily Overlapping Objects”, ISBI 2013.Proceedings, pp. 656-659, 2013.

Technical Report (711.68 KB)

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)

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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)

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. 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., 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.

Technical Report (625.06 KB)

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)

M. Schiegg, Diego, F., and Hamprecht, F. A., “Learning Diverse Models: The Coulomb Structured Support Vector Machine”, ECCV. Proceedings, vol. LNCS 9907. Springer, pp. 585-599, 2016.

Technical Report (2.54 MB)

F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation”, in NIPS. Proceedings, 2013.

Technical Report (2.79 MB)

F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation for Identifying Neuronal Activity”, in Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of Abstracts., 2013.

Technical Report (1.05 MB)

M. Weiler, Hamprecht, F. A., and Storath, M., “Learning Steerable Filters for Rotation Equivariant CNNs”, CVPR. Proceedings. pp. 849-858, 2018.

Technical Report (1.35 MB)

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 and Hamprecht, F. A., “Learning to Segment Dense Cell Nuclei with Shape Prior”, CVPR 2012. Proceedings, pp. 1012-1018, 2012.

Technical Report (2.66 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)

J. Funke, Hamprecht, F. A., and Zhang, C., “Learning to Segment: Training Hierarchical Segmentation under a Topological Loss”, in MICCAI. Proceedings, Part III, 2015, vol. 9351, pp. 268-275.

Technical Report (2.92 MB)

C. Sommer, Fiaschi, L., Hamprecht, F. A., and Gerlich, D., “Learning-based Mitotic Cell Detection in Histopathological Images”, ICPR 2012. Proceedings, pp. 2306-2309, 2012.

Technical Report (1.96 MB)

E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.

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B. H. Menze, Kelm, B. Michael, Heck, D., Lichy, M. P., and Hamprecht, F. A., “Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images”, in Bildverarbeitung für die Medizin, 2006, pp. 31-36.

Technical Report (672.84 KB)

M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Maschinelles Lernen”, Patent, Patent Number WO2017032775A1, 2017.

Technical Report (317.04 KB)

M. Staudacher, Hamprecht, F. A., and Görlitz, L., “Method for processing an intensity image of a microscope”, Patent, Patent Number: WO2008034721A1, 2008.

Technical Report (39.81 KB)

M. Kirchner, Steen, J. A. J., Hamprecht, F. A., and Steen, H., “MGFp: An Open Mascot Generic Format Parser Library Implementation”, Journal of Proteome Research, vol. 9 (5), p. 27622763, 2010.

Technical Report (125.18 KB)

B. H. Menze, Kelm, B. Michael, Weber, M. - A., Bachert, P., and Hamprecht, F. A., “Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI”, Magnetic Resonance in Medicine, vol. 59, pp. 1457-1466, 2008.

Technical Report (1.45 MB)

P. J. Gee, Hamprecht, F. A., Schuler, L. D., van Gunsteren, W. F., Duchardt, E., Schwalbe, H., Albert, M., and Seebach, D., “A molecular dynamics simulation study of the conformational preferences of oligo-(3- hydroxyalcanoic acids) in chloroform solution”, Helv. Chim. Acta, vol. 85, pp. 618-632, 2002.

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.

B. Jähne, Brocke, M., Eisele, H., Hader, S., Hamprecht, F. A., Happold, W., Raisch, F., and Restle, J., “Multidimensionale Bildverarbeitung in der Produktion”, QZ, vol. 47, p. 1154--1159, 2002.

G. Urban, Bendszus, M., Hamprecht, F. A., and Kleesiek, J., “Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution, 2014, pp. 31-35.

B. H. Menze and Hamprecht, F. A., “Multimodal Medical Image Analysis: from Visualization to Disease Modeling”, Zeitschrift für Med. Physik, vol. 1, pp. 1-2, 2010.

Technical Report (481.58 KB)

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