| Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs N Gessert, J Krüger, R Opfer, AC Ostwaldt, P Manogaran, HH Kitzler, ... Computerized Medical Imaging and Graphics 84, 101772, 2020 | 47 | 2020 |
| Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks J Krüger, R Opfer, N Gessert, AC Ostwaldt, P Manogaran, HH Kitzler, ... NeuroImage: Clinical 28, 102445, 2020 | 44 | 2020 |
| Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI M Bengs, F Behrendt, J Krüger, R Opfer, A Schlaefer International journal of computer assisted radiology and surgery 16 (9 …, 2021 | 26 | 2021 |
| 4D deep learning for multiple sclerosis lesion activity segmentation N Gessert, M Bengs, J Krüger, R Opfer, AC Ostwaldt, P Manogaran, ... arXiv preprint arXiv:2004.09216, 2020 | 17 | 2020 |
| Statistical appearance models based on probabilistic correspondences J Krüger, J Ehrhardt, H Handels Medical image analysis 37, 146-159, 2017 | 15 | 2017 |
| Simulation of mammographic breast compression in 3D MR images using ICP-based B-spline deformation for multimodality breast cancer diagnosis J Krüger, J Ehrhardt, A Bischof, H Handels International journal of computer assisted radiology and surgery 9 (3), 367-377, 2014 | 14 | 2014 |
| Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved … R Opfer, J Krüger, L Spies, AC Ostwaldt, HH Kitzler, S Schippling, ... European Radiology, 1-10, 2022 | 12 | 2022 |
| Breast compression simulation using ICP-based B-spline deformation for correspondence analysis in mammography and MRI datasets J Krüger, J Ehrhardt, A Bischof, H Handels Medical Imaging 2013: Image Processing 8669, 86691D, 2013 | 11 | 2013 |
| Registration with probabilistic correspondences—Accurate and robust registration for pathological and inhomogeneous medical data J Krüger, S Schultz, H Handels, J Ehrhardt Computer Vision and Image Understanding 190, 102839, 2020 | 10 | 2020 |
| Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction M Bengs, F Behrendt, MH Laves, J Krüger, R Opfer, A Schlaefer Medical Imaging 2022: Computer-Aided Diagnosis 12033, 291-295, 2022 | 9 | 2022 |
| Infratentorial lesions in multiple sclerosis patients: intra-and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks J Krüger, AC Ostwaldt, L Spies, B Geisler, A Schlaefer, HH Kitzler, ... European radiology 32 (4), 2798-2809, 2022 | 8 | 2022 |
| Bayesian inference for uncertainty quantification in point-based deformable image registration S Schultz, J Krüger, H Handels, J Ehrhardt Medical Imaging 2019: Image Processing 10949, 109491S, 2019 | 8 | 2019 |
| Capturing Inter-Slice Dependencies of 3D Brain MRI-Scans for Unsupervised Anomaly Detection F Behrendt, M Bengs, D Bhattacharya, J Krüger, R Opfer, A Schlaefer Medical Imaging with Deep Learning, 2022 | 6 | 2022 |
| Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data F Behrendt, M Bengs, F Rogge, J Krüger, R Opfer, A Schlaefer 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-4, 2022 | 6 | 2022 |
| Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment R Opfer, J Krüger, L Spies, HH Kitzler, S Schippling, R Buchert Neuroradiology, 1-9, 2022 | 5 | 2022 |
| Fully automated longitudinal segmentation of new or enlarging Multiple Scleroses (MS) lesions using 3D convolution neural networks J Krüger, R Opfer, N Gessert, A Ostwaldt, C Walker-Egger, P Manogaran, ... RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden …, 2020 | 5 | 2020 |
| Estimation of corresponding locations in ipsilateral mammograms: a comparison of different methods M Wilms, J Krüger, M Marx, J Ehrhardt, A Bischof, H Handels Medical Imaging 2015: Computer-Aided Diagnosis 9414, 94142B, 2015 | 5 | 2015 |
| Statistical shape and appearance models without one-to-one correspondences J Ehrhardt, J Krüger, H Handels Medical Imaging 2014: Image Processing 9034, 90340U, 2014 | 5 | 2014 |
| Age-dependent cut-offs for pathological deep gray matter and thalamic volume loss using Jacobian integration R Opfer, J Krüger, L Spies, M Hamann, CA Wicki, HH Kitzler, C Gocke, ... NeuroImage: Clinical 28, 102478, 2020 | 4 | 2020 |
| Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs F Behrendt, D Bhattacharya, J Krüger, R Opfer, A Schlaefer Current Directions in Biomedical Engineering 8 (1), 34-37, 2022 | 3 | 2022 |
Matthias WilmsUniversity of CalgaryVerified email at ucalgary.ca
