| The liver tumor segmentation benchmark (lits) P Bilic, PF Christ, E Vorontsov, G Chlebus, H Chen, Q Dou, CW Fu, X Han, ... arXiv preprint arXiv:1901.04056, 2019 | 401 | 2019 |
| Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing G Chlebus, A Schenk, JH Moltz, B van Ginneken, HK Hahn, H Meine Scientific reports 8 (1), 1-7, 2018 | 152 | 2018 |
| Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study RD Rudyanto, S Kerkstra, EM Van Rikxoort, C Fetita, PY Brillet, C Lefevre, ... Medical image analysis 18 (7), 1217-1232, 2014 | 142 | 2014 |
| Neural network-based automatic liver tumor segmentation with random forest-based candidate filtering G Chlebus, H Meine, JH Moltz, A Schenk arXiv preprint arXiv:1706.00842, 2017 | 60 | 2017 |
| Segmentation-based partial volume correction for volume estimation of solid lesions in CT F Heckel, H Meine, JH Moltz, JM Kuhnigk, JT Heverhagen, A Kießling, ... IEEE Transactions on Medical Imaging 33 (2), 462-480, 2013 | 47 | 2013 |
| Principles and methods for automatic and semi-automatic tissue segmentation in MRI data L Wang, T Chitiboi, H Meine, M Günther, HK Hahn Magnetic Resonance Materials in Physics, Biology and Medicine 29 (2), 95-110, 2016 | 41 | 2016 |
| Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections G Chlebus, H Meine, S Thoduka, N Abolmaali, B Van Ginneken, HK Hahn, ... PloS one 14 (5), e0217228, 2019 | 27 | 2019 |
| Evaluation of deep learning methods for parotid gland segmentation from CT images A Hänsch, M Schwier, T Gass, T Morgas, B Haas, V Dicken, H Meine, ... Journal of Medical Imaging 6 (1), 011005, 2018 | 23 | 2018 |
| Microstructural analysis of lignocellulosic fiber networks T Walther, K Terzic, T Donath, H Meine, F Beckmann, H Thoemen Developments in X-ray Tomography V 6318, 341-350, 2006 | 23 | 2006 |
| The GeoMap: A unified representation for topology and geometry H Meine, U Köthe International Workshop on Graph-Based Representations in Pattern Recognition …, 2005 | 22 | 2005 |
| A topological sampling theorem for robust boundary reconstruction and image segmentation H Meine, U Köthe, P Stelldinger Discrete Applied Mathematics 157 (3), 524-541, 2009 | 21 | 2009 |
| Automatic and efficient MRI-US segmentations for improving intraoperative image fusion in image-guided neurosurgery J Nitsch, J Klein, P Dammann, K Wrede, O Gembruch, JH Moltz, H Meine, ... NeuroImage: Clinical 22, 101766, 2019 | 17 | 2019 |
| Deep learning based automatic liver tumor segmentation in CT with shape-based post-processing G Chlebus, A Schenk, JH Moltz, B van Ginneken, HK Hahn, H Meine | 15 | 2018 |
| On the evaluation of segmentation editing tools F Heckel, JH Moltz, H Meine, B Geisler, A Kießling, M D’Anastasi, ... Journal of Medical Imaging 1 (3), 034005, 2014 | 14 | 2014 |
| Image segmentation with the exact watershed transform H Meine, U Köthe Proc. Vis., Imaging, and Image Processing, 400-405, 2005 | 14 | 2005 |
| Comparison of u-net-based convolutional neural networks for liver segmentation in ct H Meine, G Chlebus, M Ghafoorian, I Endo, A Schenk arXiv preprint arXiv:1810.04017, 2018 | 13 | 2018 |
| Deep learning based segmentation of organs of the female pelvis in CBCT scans for adaptive radiotherapy using CT and CBCT data A Hänsch, V Dicken, T Grass, T Morgas, J Klein, H Meine, HK Hahn Berlin: Computer Assisted Radiology and Surgery–CARS 2018, 133, 2018 | 13 | 2018 |
| Topologically correct image segmentation using alpha shapes P Stelldinger, U Köthe, H Meine International Conference on Discrete Geometry for Computer Imagery, 542-554, 2006 | 13 | 2006 |
| Anisotropic 3D multi-stream CNN for accurate prostate segmentation from multi-planar MRI A Meyer, G Chlebus, M Rak, D Schindele, M Schostak, B van Ginneken, ... Computer Methods and Programs in Biomedicine 200, 105821, 2021 | 11 | 2021 |
| Accurate CT-MR image registration for deep brain stimulation: a multi-observer evaluation study J Rühaak, A Derksen, S Heldmann, M Hallmann, H Meine Medical Imaging 2015: Image Processing 9413, 851-857, 2015 | 11 | 2015 |
