| What are normal relaxation times of tissues at 3 T? JZ Bojorquez, S Bricq, C Acquitter, F Brunotte, PM Walker, A Lalande Magnetic resonance imaging 35, 69-80, 2017 | 294 | 2017 |
| Laser speckle contrast imaging accurately measures blood flow over moving skin surfaces G Mahé, P Rousseau, S Durand, S Bricq, G Leftheriotis, P Abraham Microvascular research 81 (2), 183-188, 2011 | 120 | 2011 |
| Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains S Bricq, C Collet, JP Armspach Medical image analysis 12 (6), 639-652, 2008 | 120 | 2008 |
| Inverse problems in vision and 3D tomography A Mohamad-Djafari John Wiley & Sons, 2013 | 47 | 2013 |
| Triplet Markov chain for 3D MRI brain segmentation using a probabilistic atlas S Bricq, C Collet, JP Armspach 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006 …, 2006 | 39 | 2006 |
| MS lesion segmentation based on hidden Markov chains S Bricq, C Collet, J Armspach Grand Challenge Work.: Mult. Scler. Lesion Segm. Challenge, 1-9, 2008 | 33 | 2008 |
| Lesions detection on 3D brain MRI using trimmmed likelihood estimator and probabilistic atlas S Bricq, C Collet, JP Armspach 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to …, 2008 | 30 | 2008 |
| Semiautomatic detection of myocardial contours in order to investigate normal values of the left ventricular trabeculated mass using MRI S Bricq, J Frandon, M Bernard, M Guye, M Finas, L Marcadet, L Miquerol, ... Journal of Magnetic Resonance Imaging 43 (6), 1398-1406, 2016 | 28 | 2016 |
| Markovian segmentation of 3D brain MRI to detect multiple sclerosis lesions S Bricq, C Collet, JP Armspach 2008 15th IEEE International Conference on Image Processing, 733-736, 2008 | 24 | 2008 |
| A novel alternative to classify tissues from T 1 and T 2 relaxation times for prostate MRI JZ Bojorquez, S Bricq, F Brunotte, PM Walker, A Lalande Magnetic Resonance Materials in Physics, Biology and Medicine 29, 777-788, 2016 | 20 | 2016 |
| Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge C Martín-Isla, VM Campello, C Izquierdo, K Kushibar, C Sendra-Balcells, ... IEEE Journal of Biomedical and Health Informatics, 2023 | 19 | 2023 |
| Assessing spatial resolution versus sensitivity from laser speckle contrast imaging: application to frequency analysis S Bricq, G Mahé, D Rousseau, A Humeau-Heurtier, F Chapeau-Blondeau, ... Medical & biological engineering & computing 50, 1017-1023, 2012 | 18 | 2012 |
| MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images L Li, F Wu, S Wang, X Luo, C Martín-Isla, S Zhai, J Zhang, Y Liu, Z Zhang, ... Medical Image Analysis 87, 102808, 2023 | 17 | 2023 |
| Distribution of left ventricular trabeculation across age and gender in 140 healthy Caucasian subjects on MR imaging Z Bentatou, M Finas, P Habert, F Kober, M Guye, S Bricq, A Lalande, ... Diagnostic and interventional imaging 99 (11), 689-698, 2018 | 17 | 2018 |
| Leveraging uncertainty estimates to improve segmentation performance in cardiac MR TW Arega, S Bricq, F Meriaudeau Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and …, 2021 | 13 | 2021 |
| Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine JA Zavala Bojorquez, PM Jodoin, S Bricq, PM Walker, F Brunotte, ... PloS one 14 (2), e0211944, 2019 | 13 | 2019 |
| Using MRI-specific data augmentation to enhance the segmentation of right ventricle in multi-disease, multi-center and multi-view cardiac MRI TW Arega, F Legrand, S Bricq, F Meriaudeau International Workshop on Statistical Atlases and Computational Models of …, 2021 | 12 | 2021 |
| Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision transformer TW Arega, S Bricq, F Legrand, A Jacquier, A Lalande, F Meriaudeau Medical Image Analysis 86, 102773, 2023 | 9 | 2023 |
| An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net … K Brahim, TW Arega, A Boucher, S Bricq, A Sakly, F Meriaudeau Sensors 22 (6), 2084, 2022 | 9 | 2022 |
| Automatic myocardial scar segmentation from multi-sequence cardiac MRI using fully convolutional densenet with inception and squeeze-excitation module TW Arega, S Bricq Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge …, 2020 | 9 | 2020 |
