| An objective comparison of cell-tracking algorithms V Ulman, M Maška, KEG Magnusson, O Ronneberger, C Haubold, ... Nature methods 14 (12), 1141-1152, 2017 | 299 | 2017 |
| The Mutex Watershed: Efficient, Parameter-Free Image Partitioning S Wolf, C Pape, A Bailoni, N Rahaman, A Kreshuk, U Kothe, ... Proceedings of the European Conference on Computer Vision (ECCV), 546-562, 2018 | 33 | 2018 |
| Learned Watershed: End-to-End Learning of Seeded Segmentation S Wolf, L Schott, U Köthe, F Hamprecht International Conference on Computer Vision: ICCV 2017, 2017 | 30 | 2017 |
| The mutex watershed and its objective: Efficient, parameter-free graph partitioning S Wolf, A Bailoni, C Pape, N Rahaman, A Kreshuk, U Köthe, ... IEEE transactions on pattern analysis and machine intelligence, 2020 | 16 | 2020 |
| A generalized framework for agglomerative clustering of signed graphs applied to instance segmentation A Bailoni, C Pape, S Wolf, T Beier, A Kreshuk, FA Hamprecht arXiv preprint arXiv:1906.11713, 2019 | 10 | 2019 |
| Tracking objects with higher order interactions via delayed column generation S Wang, S Wolf, C Fowlkes, J Yarkony Artificial Intelligence and Statistics, 1132-1140, 2017 | 10* | 2017 |
| A generalized successive shortest paths solver for tracking dividing targets C Haubold, J Aleš, S Wolf, FA Hamprecht European Conference on Computer Vision, 566-582, 2016 | 10 | 2016 |
| Microscopy‐based assay for semi‐quantitative detection of SARS‐CoV‐2 specific antibodies in human sera: A semi‐quantitative, high throughput, microscopy‐based assay expands … C Pape, R Remme, A Wolny, S Olberg, S Wolf, L Cerrone, M Cortese, ... BioEssays 43 (3), 2000257, 2021 | 9 | 2021 |
| The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation S Wolf, Y Li, C Pape, A Bailoni, A Kreshuk, FA Hamprecht European Conference on Computer Vision, 208-224, 2020 | 5 | 2020 |
| LeMoNADe: learned motif and neuronal assembly detection in calcium imaging videos E Kirschbaum, M Haußmann, S Wolf, H Sonntag, J Schneider, S Elzoheiry, ... arXiv preprint arXiv:1806.09963, 2018 | 5 | 2018 |
| Learning the arrow of time for problems in reinforcement learning N Rahaman, S Wolf, A Goyal, R Remme, Y Bengio | 3 | 2020 |
| Inpainting Networks Learn to Separate Cells in Microscopy Images. S Wolf, FA Hamprecht, J Funke, H Janelia, VA Ashburn BMVC, 2020 | 3 | 2020 |
| Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models M Schiegg, B Heuer, C Haubold, S Wolf, U Koethe, FA Hamprecht 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 394-398, 2015 | 3 | 2015 |
| Instance separation emerges from inpainting S Wolf, FA Hamprecht, J Funke arXiv preprint arXiv:2003.00891, 2020 | 2 | 2020 |
| Learning the Arrow of Time N Rahaman, S Wolf, A Goyal, R Remme, Y Bengio arXiv preprint arXiv:1907.01285, 2019 | 2 | 2019 |
| Current approaches to fate mapping and lineage tracing using image data S Wolf, Y Wan, K McDole Development 148 (18), dev198994, 2021 | 1 | 2021 |
| Proposal-free volumetric instance segmentation from latent single-instance masks A Bailoni, C Pape, S Wolf, A Kreshuk, FA Hamprecht DAGM German Conference on Pattern Recognition, 331-344, 2020 | 1 | 2020 |
| Tracking objects with higher order interactions using delayed column generation S Wolf, FA Hamprecht, J Yarkony arXiv preprint arXiv:1512.02413, 2015 | 1 | 2015 |
| Machine Learning for Instance Segmentation S Wolf | | 2020 |
| A GAN framework for Instance Segmentation using the Mutex Watershed Algorithm M Vikram, S Wolf | | |
Fred A. HamprechtProfessor, Heidelberg UniversityVerified email at iwr.uni-heidelberg.de
