| ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI O Maier, BH Menze, J Von der Gablentz, L Häni, MP Heinrich, M Liebrand, ... Medical image analysis 35, 250-269, 2017 | 522 | 2017 |
| Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences O Maier, M Wilms, J von der Gablentz, UM Krämer, TF Münte, H Handels Journal of neuroscience methods 240, 89-100, 2015 | 194 | 2015 |
| Training CNNs for image registration from few samples with model-based data augmentation H Uzunova, M Wilms, H Handels, J Ehrhardt Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 138 | 2017 |
| Supervised machine learning tools: a tutorial for clinicians LL Vercio, K Amador, JJ Bannister, S Crites, A Gutierrez, ME MacDonald, ... Journal of Neural Engineering 17 (6), 062001, 2020 | 122 | 2020 |
| Statistical shape modeling of the left ventricle: myocardial infarct classification challenge A Suinesiaputra, P Ablin, X Alba, M Alessandrini, J Allen, W Bai, S Cimen, ... IEEE journal of biomedical and health informatics 22 (2), 503-515, 2017 | 93 | 2017 |
| Model-based sparse-to-dense image registration for realtime respiratory motion estimation in image-guided interventions IY Ha, M Wilms, H Handels, MP Heinrich IEEE Transactions on Biomedical Engineering 66 (2), 302-310, 2018 | 47 | 2018 |
| Multivariate regression approaches for surrogate-based diffeomorphic estimation of respiratory motion in radiation therapy M Wilms, R Werner, J Ehrhardt, A Schmidt-Richberg, HP Schlemmer, ... Physics in Medicine & Biology 59 (5), 1147, 2014 | 40 | 2014 |
| Direct visuo-haptic 4D volume rendering using respiratory motion models D Fortmeier, M Wilms, A Mastmeyer, H Handels IEEE transactions on haptics 8 (4), 371-383, 2015 | 39 | 2015 |
| Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers O Maier, M Wilms, J von der Gablentz, U Krämer, H Handels Medical Imaging 2014: Computer-Aided Diagnosis 9035, 21-32, 2014 | 36 | 2014 |
| Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions P Mouches, M Wilms, D Rajashekar, S Langner, ND Forkert Human brain mapping 43 (8), 2554-2566, 2022 | 35 | 2022 |
| Multi-resolution multi-object statistical shape models based on the locality assumption M Wilms, H Handels, J Ehrhardt Medical image analysis 38, 17-29, 2017 | 34 | 2017 |
| Real-Time Ultrasound Simulation for Training of US-Guided Needle Insertion in Breathing Virtual Patients A Mastmeyer, M Wilms, D Fortmeier, J Schröder, H Handels Medicine Meets Virtual Reality 22: NextMed/MMVR22 220, 219, 2016 | 33 | 2016 |
| Image features for brain lesion segmentation using random forests O Maier, M Wilms, H Handels Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 27 | 2016 |
| Fully automatic landmarking of syndromic 3D facial surface scans using 2D images JJ Bannister, SR Crites, JD Aponte, DC Katz, M Wilms, OD Klein, ... Sensors 20 (11), 3171, 2020 | 26 | 2020 |
| High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly D Rajashekar, M Wilms, ME MacDonald, J Ehrhardt, P Mouches, ... Scientific Data 7 (1), 56, 2020 | 26 | 2020 |
| Interpatient respiratory motion model transfer for virtual reality simulations of liver punctures A Mastmeyer, M Wilms, H Handels arXiv preprint arXiv:1707.08554, 2017 | 23 | 2017 |
| Explainable classification of Parkinson’s disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets M Camacho, M Wilms, P Mouches, H Almgren, R Souza, R Camicioli, ... NeuroImage: Clinical 38, 103405, 2023 | 20 | 2023 |
| Fairness-related performance and explainability effects in deep learning models for brain image analysis EAM Stanley, M Wilms, P Mouches, ND Forkert Journal of Medical Imaging 9 (6), 061102-061102, 2022 | 20 | 2022 |
| A 4D statistical shape model for automated segmentation of lungs with large tumors M Wilms, J Ehrhardt, H Handels Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012: 15th …, 2012 | 20 | 2012 |
| An analysis of the vulnerability of two common deep learning-based medical image segmentation techniques to model inversion attacks N Subbanna, M Wilms, A Tuladhar, ND Forkert Sensors 21 (11), 3874, 2021 | 19 | 2021 |
