| 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 | 2292 | 2018 |
| 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 (1), 65, 2018 | 746 | 2018 |
| Ensembles of multiple models and architectures for robust brain tumour segmentation K Kamnitsas, W Bai, E Ferrante, S McDonagh, M Sinclair, N Pawlowski, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 595 | 2018 |
| Semi-supervised learning for network-based cardiac MR image segmentation W Bai, O Oktay, M Sinclair, H Suzuki, M Rajchl, G Tarroni, B Glocker, ... Medical Image Computing and Computer-Assisted Intervention− MICCAI 2017 …, 2017 | 514 | 2017 |
| 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 | 194 | 2020 |
| A novel porous mechanical framework for modelling the interaction between coronary perfusion and myocardial mechanics AN Cookson, J Lee, C Michler, R Chabiniok, E Hyde, DA Nordsletten, ... Journal of biomechanics 45 (5), 850-855, 2012 | 110 | 2012 |
| A computationally efficient framework for the simulation of cardiac perfusion using a multi‐compartment Darcy porous‐media flow model C Michler, AN Cookson, R Chabiniok, E Hyde, J Lee, M Sinclair, T Sochi, ... International journal for numerical methods in biomedical engineering 29 (2 …, 2013 | 107 | 2013 |
| An automatic service for the personalization of ventricular cardiac meshes P Lamata, M Sinclair, E Kerfoot, A Lee, A Crozier, B Blazevic, S Land, ... Journal of The Royal Society Interface 11 (91), 20131023, 2014 | 87 | 2014 |
| Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks M Sinclair, CF Baumgartner, J Matthew, W Bai, JC Martinez, Y Li, S Smith, ... 2018 40th annual international conference of the IEEE engineering in …, 2018 | 74 | 2018 |
| Standard plane detection in 3d fetal ultrasound using an iterative transformation network Y Li, B Khanal, B Hou, A Alansary, JJ Cerrolaza, M Sinclair, J Matthew, ... International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 70 | 2018 |
| Myocardial perfusion simulation for coronary artery disease: a coupled patient-specific multiscale model L Papamanolis, HJ Kim, C Jaquet, M Sinclair, M Schaap, I Danad, ... Annals of biomedical engineering 49, 1432-1447, 2021 | 58 | 2021 |
| Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging Q Meng, M Sinclair, V Zimmer, B Hou, M Rajchl, N Toussaint, O Oktay, ... IEEE transactions on medical imaging 38 (12), 2755-2767, 2019 | 58 | 2019 |
| A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: application to cardiac resynchronisation therapy response prediction D Peressutti, M Sinclair, W Bai, T Jackson, J Ruijsink, D Nordsletten, ... Medical image analysis 35, 669-684, 2017 | 54 | 2017 |
| Deep learning with ultrasound physics for fetal skull segmentation JJ Cerrolaza, M Sinclair, Y Li, A Gomez, E Ferrante, J Matthew, C Gupta, ... 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018 | 48 | 2018 |
| Fast multiple landmark localisation using a patch-based iterative network Y Li, A Alansary, JJ Cerrolaza, B Khanal, M Sinclair, J Matthew, C Gupta, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 48 | 2018 |
| Multi-scale parameterisation of a myocardial perfusion model using whole-organ arterial networks ER Hyde, AN Cookson, J Lee, C Michler, A Goyal, T Sochi, R Chabiniok, ... Annals of biomedical engineering 42, 797-811, 2014 | 46 | 2014 |
| Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control E Puyol-Antón, B Ruijsink, CF Baumgartner, PG Masci, M Sinclair, ... Journal of Cardiovascular Magnetic Resonance 22 (1), 60, 2020 | 45 | 2020 |
| Maxstyle: Adversarial style composition for robust medical image segmentation C Chen, Z Li, C Ouyang, M Sinclair, W Bai, D Rueckert International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 43 | 2022 |
| Atlas-ISTN: joint segmentation, registration and atlas construction with image-and-spatial transformer networks M Sinclair, A Schuh, K Hahn, K Petersen, Y Bai, J Batten, M Schaap, ... Medical Image Analysis 78, 102383, 2022 | 42 | 2022 |
| A multimodal spatiotemporal cardiac motion atlas from MR and ultrasound data E Puyol-Anton, M Sinclair, B Gerber, MS Amzulescu, H Langet, ... Medical image analysis 40, 96-110, 2017 | 41* | 2017 |
Daniel RueckertTechnical University of Munich and Imperial College Londonยืนยันอีเมลแล้วที่ tum.de