| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 857 | 2018 |
| Anatomically constrained neural networks (ACNN): Application to cardiac image enhancement and segmentation O Oktay, E Ferrante, K Kamnitsas, M Heinrich, W Bai, J Caballero, S Cook, ... IEEE Transactions on Medical Imaging 37 (2), 384-395, 2018 | 546 | 2018 |
| Ensembles of multiple models and architectures for robust brain tumour segmentation K Kamnitsas, W Bai, E Ferrante, S McDonagh, M Sinclair, N Pawlowski, ... MICCAI Brainlesion Workshop, 450-462, 2017 | 387 | 2017 |
| Automated cardiovascular magnetic resonance image analysis with fully convolutional networks W Bai, M Sinclair, G Tarroni, O Oktay, M Rajchl, G Vaillant, AM Lee, ... Journal of Cardiovascular Magnetic Resonance 20 (65), 2018 | 382 | 2018 |
| Deep learning for cardiac image segmentation: A review C Chen, C Qin, H Qiu, G Tarroni, J Duan, W Bai, D Rueckert Frontiers in Cardiovascular Medicine, 2020 | 281 | 2020 |
| DeepCut: Object segmentation from bounding box annotations using convolutional neural networks M Rajchl, MCH Lee, O Oktay, K Kamnitsas, J Passerat-Palmbach, W Bai, ... IEEE Transactions on Medical Imaging 36 (2), 674-683, 2017 | 280 | 2017 |
| A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: Application to cardiac MR images W Bai, W Shi, DP O'Regan, T Tong, H Wang, S Jamil-Copley, NS Peters, ... IEEE Transactions on Medical Imaging 32 (7), 1302-1315, 2013 | 232 | 2013 |
| Cardiac image super-resolution with global correspondence using multi-atlas patchmatch W Shi, J Caballero, C Ledig, X Zhuang, W Bai, K Bhatia, ... International Conference on Medical Image Computing and Computer-Assisted …, 2013 | 227 | 2013 |
| Semi-supervised learning for network-based cardiac MR image segmentation W Bai, O Oktay, M Sinclair, H Suzuki, M Rajchl, G Tarroni, B Glocker, ... International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 220 | 2017 |
| Multi-input cardiac image super-resolution using convolutional neural networks O Oktay, W Bai, M Lee, R Guerrero, K Kamnitsas, J Caballero, ... International Conference on Medical Image Computing and Computer-Assisted …, 2016 | 207 | 2016 |
| Right ventricle segmentation from cardiac MRI: A collation study C Petitjean, MA Zuluaga, W Bai, JN Dacher, D Grosgeorge, J Caudron, ... Medical Image Analysis 19 (1), 187-202, 2014 | 201 | 2014 |
| Multi-atlas segmentation with augmented features for cardiac MR images W Bai, W Shi, C Ledig, D Rueckert Medical Image Analysis 19 (1), 98-109, 2014 | 161 | 2014 |
| Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand … R Karim, RJ Housden, M Balasubramaniam, Z Chen, D Perry, A Uddin, ... Journal of Cardiovascular Magnetic Resonance 15 (105), 1, 2013 | 149 | 2013 |
| Automated analysis of atrial late gadolinium enhancement imaging that correlates with endocardial voltage and clinical outcomes: A 2-center study LC Malcolme-Lawes, C Juli, R Karim, W Bai, R Quest, PB Lim, ... Heart Rhythm 10 (8), 1184-1191, 2013 | 141 | 2013 |
| Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach J Duan, G Bello, J Schlemper, W Bai, TJW Dawes, C Biffi, A de Marvao, ... IEEE Transactions on Medical Imaging 38 (9), 2151-2164, 2019 | 117 | 2019 |
| A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion W Bai, W Shi, A de Marvao, TJW Dawes, DP O’Regan, SA Cook, ... Medical Image Analysis 26 (1), 133-145, 2015 | 111 | 2015 |
| Joint learning of motion estimation and segmentation for cardiac MR image sequences C Qin, W Bai, J Schlemper, SE Petersen, SK Piechnik, S Neubauer, ... International Conference on Medical Image Computing and Computer Assisted …, 2018 | 99 | 2018 |
| 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, ... JACC: Cardiovascular Imaging, 2019 | 97 | 2019 |
| Regularized B-spline deformable registration for respiratory motion correction in PET images W Bai, M Brady Physics in Medicine & Biology 54 (9), 2719, 2009 | 93 | 2009 |
| Motion correction and attenuation correction for respiratory gated PET images W Bai, M Brady IEEE Transactions on Medical Imaging 30 (2), 351-365, 2011 | 91 | 2011 |
Daniel RueckertTechnical University of Munich and Imperial College LondonVerified email at tum.de
