| MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration MP Heinrich, M Jenkinson, M Bhushan, T Matin, FV Gleeson, M Brady, ... Medical image analysis 16 (7), 1423-1435, 2012 | 704 | 2012 |
| Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration D Rueckert, AF Frangi, JA Schnabel IEEE transactions on medical imaging 22 (8), 1014-1025, 2003 | 569 | 2003 |
| Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling AF Frangi, D Rueckert, JA Schnabel, WJ Niessen IEEE transactions on medical imaging 21 (9), 1151-1166, 2002 | 556 | 2002 |
| A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations JA Schnabel, D Rueckert, M Quist, JM Blackall, AD Castellano-Smith, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2001: 4th …, 2001 | 536 | 2001 |
| Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge K Murphy, B Van Ginneken, JM Reinhardt, S Kabus, K Ding, X Deng, ... IEEE transactions on medical imaging 30 (11), 1901-1920, 2011 | 511 | 2011 |
| Reconstruction of fetal brain MRI with intensity matching and complete outlier removal M Kuklisova-Murgasova, G Quaghebeur, MA Rutherford, JV Hajnal, ... Medical image analysis 16 (8), 1550-1564, 2012 | 418 | 2012 |
| Validation of nonrigid image registration using finite-element methods: application to breast MR images JA Schnabel, C Tanner, AD Castellano-Smith, A Degenhard, MO Leach, ... IEEE transactions on medical imaging 22 (2), 238-247, 2003 | 378 | 2003 |
| MRF-based deformable registration and ventilation estimation of lung CT MP Heinrich, M Jenkinson, M Brady, JA Schnabel IEEE transactions on medical imaging 32 (7), 1239-1248, 2013 | 314 | 2013 |
| Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer ML Giger, N Karssemeijer, JA Schnabel Annual review of biomedical engineering 15, 327-357, 2013 | 265 | 2013 |
| An evaluation of four automatic methods of segmenting the subcortical structures in the brain KO Babalola, B Patenaude, P Aljabar, J Schnabel, D Kennedy, W Crum, ... Neuroimage 47 (4), 1435-1447, 2009 | 254 | 2009 |
| A topological loss function for deep-learning based image segmentation using persistent homology JR Clough, N Byrne, I Oksuz, VA Zimmer, JA Schnabel, AP King IEEE transactions on pattern analysis and machine intelligence 44 (12), 8766 …, 2020 | 231 | 2020 |
| Automatic construction of 3D statistical deformation models using non-rigid registration D Rueckert, AF Frangi, JA Schnabel Medical Image Computing and Computer-Assisted Intervention–MICCAI 2001: 4th …, 2001 | 227 | 2001 |
| Towards realtime multimodal fusion for image-guided interventions using self-similarities MP Heinrich, M Jenkinson, BW Papież, SM Brady, JA Schnabel Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013 | 206 | 2013 |
| Deep learning for PET image reconstruction AJ Reader, G Corda, A Mehranian, C da Costa-Luis, S Ellis, JA Schnabel IEEE Transactions on Radiation and Plasma Medical Sciences 5 (1), 1-25, 2020 | 189 | 2020 |
| Objective assessment of deformable image registration in radiotherapy: a multi‐institution study R Kashani, M Hub, JM Balter, ML Kessler, L Dong, L Zhang, L Xing, Y Xie, ... Medical physics 35 (12), 5944-5953, 2008 | 188 | 2008 |
| Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging GJM Parker, JA Schnabel, MR Symms, DJ Werring, GJ Barker Journal of Magnetic Resonance Imaging: An Official Journal of the …, 2000 | 185 | 2000 |
| Left-ventricle quantification using residual U-Net E Kerfoot, J Clough, I Oksuz, J Lee, AP King, JA Schnabel Statistical Atlases and Computational Models of the Heart. Atrial …, 2019 | 164 | 2019 |
| Factors influencing the accuracy of biomechanical breast models C Tanner, JA Schnabel, DLG Hill, DJ Hawkes, MO Leach, DR Hose Medical physics 33 (6Part1), 1758-1769, 2006 | 164 | 2006 |
| Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function B Ruijsink, E Puyol-Antón, I Oksuz, M Sinclair, W Bai, JA Schnabel, ... Cardiovascular Imaging 13 (3), 684-695, 2020 | 163 | 2020 |
| Registration-based interpolation GP Penney, JA Schnabel, D Rueckert, MA Viergever, WJ Niessen IEEE transactions on medical imaging 23 (7), 922-926, 2004 | 155 | 2004 |
Daniel RueckertTechnical University of Munich and Imperial College London在 tum.de 的电子邮件经过验证
