| Ilastik: Interactive learning and segmentation toolkit C Sommer, C Straehle, U Koethe, FA Hamprecht 2011 IEEE international symposium on biomedical imaging: From nano to macro …, 2011 | 1219 | 2011 |
| Ilastik: interactive machine learning for (bio) image analysis S Berg, D Kutra, T Kroeger, CN Straehle, BX Kausler, C Haubold, ... Nature methods 16 (12), 1226-1232, 2019 | 969 | 2019 |
| Analyzing inverse problems with invertible neural networks L Ardizzone, J Kruse, S Wirkert, D Rahner, EW Pellegrini, RS Klessen, ... arXiv preprint arXiv:1808.04730, 2018 | 355 | 2018 |
| On oblique random forests BH Menze, BM Kelm, DN Splitthoff, U Koethe, FA Hamprecht Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 283 | 2011 |
| Learning to count with regression forest and structured labels L Fiaschi, U Köthe, R Nair, FA Hamprecht Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 236 | 2012 |
| Edge and junction detection with an improved structure tensor U Köthe Pattern Recognition: 25th DAGM Symposium, Magdeburg, Germany, September 10 …, 2003 | 232 | 2003 |
| Guided image generation with conditional invertible neural networks L Ardizzone, C Lüth, J Kruse, C Rother, U Köthe arXiv preprint arXiv:1907.02392, 2019 | 184 | 2019 |
| Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images A Kreshuk, CN Straehle, C Sommer, U Koethe, M Cantoni, G Knott, ... PloS one 6 (10), e24899, 2011 | 153 | 2011 |
| Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras M Frank, M Plaue, H Rapp, U Köthe, B Jähne, FA Hamprecht Optical Engineering 48 (1), 013602-013602-16, 2009 | 136 | 2009 |
| Segmentation of SBFSEM volume data of neural tissue by hierarchical classification B Andres, U Köthe, M Helmstaedter, W Denk, FA Hamprecht Pattern Recognition: 30th DAGM Symposium Munich, Germany, June 10-13, 2008 …, 2008 | 133 | 2008 |
| Multicut brings automated neurite segmentation closer to human performance T Beier, C Pape, N Rahaman, T Prange, S Berg, DD Bock, A Cardona, ... Nature methods 14 (2), 101-102, 2017 | 131 | 2017 |
| Probabilistic image segmentation with closedness constraints B Andres, JH Kappes, T Beier, U Köthe, FA Hamprecht 2011 International Conference on Computer Vision, 2611-2618, 2011 | 118 | 2011 |
| Globally optimal closed-surface segmentation for connectomics B Andres, T Kroeger, KL Briggman, W Denk, N Korogod, G Knott, ... Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 116 | 2012 |
| Graphical model for joint segmentation and tracking of multiple dividing cells M Schiegg, P Hanslovsky, C Haubold, U Koethe, L Hufnagel, ... Bioinformatics 31 (6), 948-956, 2015 | 100 | 2015 |
| Toward digital staining using imaging mass spectrometry and random forests M Hanselmann, U Kothe, M Kirchner, BY Renard, ER Amstalden, ... Journal of proteome research 8 (7), 3558-3567, 2009 | 94 | 2009 |
| Integrated Edge and Junction Detection with the Boundary Tensor. U Köthe ICCV 3, 424-431, 2003 | 84 | 2003 |
| Can virtual contrast enhancement in brain MRI replace gadolinium?: a feasibility study J Kleesiek, JN Morshuis, F Isensee, K Deike-Hofmann, D Paech, ... Investigative radiology 54 (10), 653-660, 2019 | 80 | 2019 |
| Disentanglement by nonlinear ica with general incompressible-flow networks (gin) P Sorrenson, C Rother, U Köthe arXiv preprint arXiv:2001.04872, 2020 | 79 | 2020 |
| DALSA: domain adaptation for supervised learning from sparsely annotated MR images M Goetz, C Weber, F Binczyk, J Polanska, R Tarnawski, ... IEEE transactions on medical imaging 35 (1), 184-196, 2015 | 77 | 2015 |
| BayesFlow: Learning complex stochastic models with invertible neural networks ST Radev, UK Mertens, A Voss, L Ardizzone, U Köthe IEEE transactions on neural networks and learning systems 33 (4), 1452-1466, 2020 | 74 | 2020 |
Fred A. HamprechtProfessor, Heidelberg UniversityVerified email at iwr.uni-heidelberg.de
