| Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study J Betancur, F Commandeur, M Motlagh, T Sharir, AJ Einstein, S Bokhari, ... JACC: Cardiovascular Imaging 11 (11), 1654-1663, 2018 | 106 | 2018 |
| Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets IEEE Transactions on Medical Imaging, 1-1, 2015 | 101* | 2015 |
| Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning J Betancur, Y Otaki, M Motwani, MB Fish, M Lemley, D Dey, H Gransar, ... JACC: Cardiovascular Imaging 11 (7), 1000-1009, 2018 | 74 | 2018 |
| Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study D Dey, S Gaur, KA Ovrehus, PJ Slomka, J Betancur, M Goeller, MM Hell, ... European radiology 28 (6), 2655-2664, 2018 | 63 | 2018 |
| Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT F Commandeur, M Goeller, J Betancur, S Cadet, M Doris, X Chen, ... IEEE transactions on medical imaging 37 (8), 1835-1846, 2018 | 57 | 2018 |
| Deep learning analysis of upright-supine high-efficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study J Betancur, LH Hu, F Commandeur, T Sharir, AJ Einstein, MB Fish, ... Journal of Nuclear Medicine 60 (5), 664-670, 2019 | 35 | 2019 |
| Rationale and design of the RE gistry of F ast Myocardial Perfusion I maging with NE xt generation SPECT (REFINE SPECT) PJ Slomka, J Betancur, JX Liang, Y Otaki, LH Hu, T Sharir, S Dorbala, ... Journal of Nuclear Cardiology 27 (3), 1010-1021, 2020 | 32 | 2020 |
| Automatic valve plane localization in myocardial perfusion SPECT/CT by machine learning: anatomic and clinical validation J Betancur, M Rubeaux, TA Fuchs, Y Otaki, Y Arnson, L Slipczuk, ... Journal of Nuclear Medicine 58 (6), 961-967, 2017 | 30 | 2017 |
| 5-year prognostic value of quantitative versus visual MPI in subtle perfusion defects: results from REFINE SPECT Y Otaki, J Betancur, T Sharir, LH Hu, H Gransar, JX Liang, PN Azadani, ... Cardiovascular Imaging 13 (3), 774-785, 2020 | 23 | 2020 |
| Segmentación de frutos de café mediante métodos de crecimiento de regiones. JAB Acevedo, FAP Ortiz, GAO Londoño Revista Facultad Nacional de Agronomía Medellín 59 (1), 3311, 2006 | 20 | 2006 |
| Digital image processing for classification of coffee cherries Z Sandoval, F Prieto, J Betancur 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference, 417-421, 2010 | 15 | 2010 |
| Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry LH Hu, J Betancur, T Sharir, AJ Einstein, S Bokhari, MB Fish, TD Ruddy, ... European Heart Journal-Cardiovascular Imaging 21 (5), 549-559, 2020 | 12 | 2020 |
| Machine learning in predicting coronary heart disease and cardiovascular disease events: results from the multi-ethnic study of atherosclerosis (mesa) R Nakanishi, D Dey, F Commandeur, P Slomka, J Betancur, H Gransar, ... Journal of the American College of Cardiology 71 (11S), A1483-A1483, 2018 | 11 | 2018 |
| Multi-atlas-based segmentation of the left atrium and pulmonary veins Z Sandoval, J Betancur, JL Dillenseger International Workshop on Statistical Atlases and Computational Models of …, 2013 | 10 | 2013 |
| Active contour-based segmentation of coffee cherries images J Betancur, F Prieto 2008 9th International Conference on Signal Processing, 2738-2741, 2008 | 10 | 2008 |
| Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. JACC Cardiovasc Imaging. 2018; 11: 1654-63 J Betancur, F Commandeur, M Motlagh, T Sharir, AJ Einstein, S Bokhari | 8 | |
| Spatio-temporal Registration of 2D US and 3D MR Images for the Characterization of Hypertrophic Cardiomyopathy J Betancur, F Schnell, A Simon, F Tavard, E Donal, A Hernández, ... International Conference on Functional Imaging and Modeling of the Heart …, 2013 | 7 | 2013 |
| Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: a report from REFINE SPECT registry LH Hu, T Sharir, RJH Miller, AJ Einstein, MB Fish, TD Ruddy, S Dorbala, ... Journal of Nuclear Cardiology 27 (4), 1180-1189, 2020 | 6 | 2020 |
| Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. JACC Cardiovasc Imaging. 2017 J Betancur, Y Otaki, M Motwani, M Fish, M Lemley, D Dey doi. org/10.1016/j. jcmg 24, 2017 | 6 | 2017 |
| Synchronization and Registration of cine Magnetic Resonance and dynamic Computed Tomography images of the Heart M Betancur, J. and Simon, A. and Langella, B. and Leclercq, C. and Hernandez ... Biomedical and Health Informatics, IEEE Journal of 99 (99), 1-1, 2015 | 6* | 2015 |
Piotr SlomkaProfessor of Medicine, Scientist, Cedars-Sinai Medical Center, UCLAVerified email at cshs.org
