| 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 | 2314 | 2018 |
| A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging Z Xiong, Q Xia, Z Hu, N Huang, C Bian, Y Zheng, S Vesal, N Ravikumar, ... Medical image analysis 67, 101832, 2021 | 356 | 2021 |
| Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge HJ Kuijf, JM Biesbroek, J De Bresser, R Heinen, S Andermatt, M Bento, ... IEEE transactions on medical imaging 38 (11), 2556-2568, 2019 | 345 | 2019 |
| VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images A Sekuboyina, ME Husseini, A Bayat, M Löffler, H Liebl, H Li, G Tetteh, ... Medical image analysis 73, 102166, 2021 | 295 | 2021 |
| Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge L Wang, D Nie, G Li, É Puybareau, J Dolz, Q Zhang, F Wang, J Xia, Z Wu, ... IEEE transactions on medical imaging 38 (9), 2219-2230, 2019 | 207 | 2019 |
| PAIP 2019: Liver cancer segmentation challenge YJ Kim, H Jang, K Lee, S Park, SG Min, C Hong, JH Park, K Lee, J Kim, ... Medical image analysis 67, 101854, 2021 | 118 | 2021 |
| QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... The journal of machine learning for biomedical imaging 2022, https://www …, 2022 | 54 | 2022 |
| Segmentation of gliomas and prediction of patient overall survival: a simple and fast procedure E Puybareau, G Tochon, J Chazalon, J Fabrizio Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 43 | 2019 |
| 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 | 35 | 2023 |
| Real-time document detection in smartphone videos E Puybareau, T Géraud 2018 25th IEEE International Conference on Image Processing (ICIP), 1498-1502, 2018 | 31 | 2018 |
| Left atrial segmentation in a few seconds using fully convolutional network and transfer learning É Puybareau, Z Zhao, Y Khoudli, E Carlinet, Y Xu, J Lacotte, T Géraud International Workshop on Statistical Atlases and Computational Models of …, 2018 | 29 | 2018 |
| Why is the winner the best? M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, S Ali, ... Proceedings of the IEEE/CVF conference on computer vision and Pattern …, 2023 | 28 | 2023 |
| Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021 CH Sudre, K Van Wijnen, F Dubost, H Adams, D Atkinson, F Barkhof, ... Medical Image Analysis 91, 103029, 2024 | 21 | 2024 |
| White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning Y Xu, T Géraud, É Puybareau, I Bloch, J Chazalon International MICCAI Brainlesion Workshop, 501-514, 2017 | 20 | 2017 |
| Going beyond p-convolutions to learn grayscale morphological operators A Kirszenberg, G Tochon, É Puybareau, J Angulo International Conference on Discrete Geometry and Mathematical Morphology …, 2021 | 19 | 2021 |
| Automated heart rate estimation in fish embryo E Puybareau, H Talbot, M Léonard 2015 International Conference on Image Processing Theory, Tools and …, 2015 | 19 | 2015 |
| An automated assay for the assessment of cardiac arrest in fish embryo É Puybareau, D Genest, E Barbeau, M Léonard, H Talbot Computers in biology and medicine 81, 32-44, 2017 | 14 | 2017 |
| A regionalized automated measurement of ciliary beating frequency E Puybareau, H Talbot, G Pelle, B Louis, JF Papon, A Coste, L Najman 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 528-531, 2015 | 14 | 2015 |
| The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high … Y Xu, B Morel, S Dahdouh, É Puybareau, A Virzì, H Urien, T Géraud, ... Medical Image Analysis 48, 75-94, 2018 | 12 | 2018 |
| Learning grayscale mathematical morphology with smooth morphological layers R Hermary, G Tochon, É Puybareau, A Kirszenberg, J Angulo Journal of Mathematical Imaging and Vision 64 (7), 736-753, 2022 | 11 | 2022 |
