Hans Meine
Hans Meine
University of Bremen & Fraunhofer MEVIS
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
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
The GeoMap: A unified representation for topology and geometry
H Meine, U Köthe
International Workshop on Graph-Based Representations in Pattern Recognition …, 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
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
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
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
Image segmentation with the exact watershed transform
H Meine, U Köthe
Proc. Vis., Imaging, and Image Processing, 400-405, 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
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
Topologically correct image segmentation using alpha shapes
P Stelldinger, U Köthe, H Meine
International Conference on Discrete Geometry for Computer Imagery, 542-554, 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
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
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