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Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). How to Extract the Geometry and Topology from Very Large 3D Segmentations. ArXiv e-prints. http://arxiv.org/abs/1009.6215

Technical Report (1.44 MB)

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

Technical Report (405.91 KB)

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

Berg, S, 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 (2019). ilastik: interactive machine learning for (bio)image analysis. Nature Methods. 16 1226-1232

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

Technical Report (466.61 KB)

Meijering, E, Carpenter, A E, Peng, H, Hamprecht, F A and Olivo-Marin, J (2016). Imagining the future of bioimage analysis. Nature Biotechnology. 34 1250-1255

Technical Report (924.57 KB)

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

Technical Report (2.25 MB)

Kandemir, M and Hamprecht, F A (2014). Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI. Proceedings

Technical Report (4.26 MB)

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

Technical Report (711.68 KB)

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

Technical Report (1.18 MB)

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Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2012). The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models. Computer Vision - {ECCV} 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part {VII}. http://dx.doi.org/10.1007/978-3-642-33786-4_12

Technical Report (446.28 KB)

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

Technical Report (532.64 KB)

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

Andres, B, Kappes, J H, Köthe, U and Hamprecht, F A (2010). The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search. ArXiv e-prints. http://arxiv.org/abs/1009.4102

Technical Report (625.06 KB)

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

Technical Report (3.76 MB)

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

Technical Report (2.54 MB)

Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation. NIPS. Proceedings. http://papers.nips.cc/paper/5076-learning-multi-level-sparse-representations

Technical Report (2.79 MB)

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

Technical Report (1.05 MB)

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

Technical Report (1.35 MB)

Fiaschi, L, Nair, R, Köthe, U and Hamprecht, F A (2012). Learning to Count with Regression Forest and Structured Labels. ICPR 2012. Proceedings. 2685-2688

Technical Report (3.66 MB)

Lou, X and Hamprecht, F A (2012). Learning to Segment Dense Cell Nuclei with Shape Prior. CVPR 2012. Proceedings. 1012-1018

Technical Report (2.66 MB)

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

Technical Report (2.87 MB)

Funke, J, Hamprecht, F A and Zhang, C (2015). Learning to Segment: Training Hierarchical Segmentation under a Topological Loss. MICCAI. Proceedings, Part III. Springer. 9351 268-275

Technical Report (2.92 MB)

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

Technical Report (1.96 MB)

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

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

Technical Report (672.84 KB)

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

Technical Report (317.04 KB)

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

Technical Report (39.81 KB)

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

Technical Report (125.18 KB)

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

Technical Report (1.45 MB)

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

Beier, T, 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 (2017). Multicut brings automated neurite segmentation closer to human performance. Nature Methods. 14 101-102. http://rdcu.be/oVDQ

Jähne, B, Brocke, M, Eisele, H, Hader, S, Hamprecht, F A, Happold, W, Raisch, F and Restle, J (2002). Multidimensionale Bildverarbeitung in der Produktion. QZ. 47 1154--1159. http://www.qz-online.de/qz-zeitschrift/archiv/artikel/multidimensionale-bildverarbeitung-in-der-produktion-fuer-anspruchsvolle-338129.html

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

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

Technical Report (481.58 KB)

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