Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Tewodros Weldebirhan Arega"'
Autor:
Khawla Brahim, Tewodros Weldebirhan Arega, Arnaud Boucher, Stephanie Bricq, Anis Sakly, Fabrice Meriaudeau
Publikováno v:
Sensors, Vol 22, Iss 6, p 2084 (2022)
Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classifica
Externí odkaz:
https://doaj.org/article/6a3608e2a382443ba1fd139c5a11004c
Autor:
Lei Li, Fuping Wu, Sihan Wang, Xinzhe Luo, Carlos Martín-Isla, Shuwei Zhai, Jianpeng Zhang, Yanfei Liu, Zhen Zhang, Markus J. Ankenbrand, Haochuan Jiang, Xiaoran Zhang, Linhong Wang, Tewodros Weldebirhan Arega, Elif Altunok, Zhou Zhao, Feiyan Li, Jun Ma, Xiaoping Yang, Elodie Puybareau, Ilkay Oksuz, Stephanie Bricq, Weisheng Li, Kumaradevan Punithakumar, Sotirios A. Tsaftaris, Laura M. Schreiber, Mingjing Yang, Guocai Liu, Yong Xia, Guotai Wang, Sergio Escalera, Xiahai Zhuang
Publikováno v:
Li, L, Wu, F, Wang, S, Luo, X, Martin-Isla, C, Zhai, S, Zhang, J, Liu7, Y, Zhang, Z, Ankenbrand, M J, Jiang, H, Zhang, X, Wang, L, Arega, T W, Altunok, E, Zhao, Z, Li, F, Ma, J, Yang, X, Puybareau, E, Oksuz, I, Bricq, S, Li, W, Punithakumar, K, Tsaftaris, S A, Schreiber, L M, Yang, M, Liu, G, Xia, Y, Wang, G, Escalera, S & Zhuang, X 2023, ' MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images ', Medical Image Analysis, vol. 87, 102808 . https://doi.org/10.1016/j.media.2023.102808
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e8087d7c379175b72701f28fa025e11
https://www.pure.ed.ac.uk/ws/files/341860919/2201.03186v1.pdf
https://www.pure.ed.ac.uk/ws/files/341860919/2201.03186v1.pdf
Autor:
Carlos Martín-Isla, Víctor M. Campello, Cristian Izquierdo, Kaisar Kushibar, Carla Sendra-Balcells, Polyxeni Gkontra, Alireza Sojoudi, Mitchell J Fulton, Tewodros Weldebirhan Arega, Kumaradevan Punithakumar, Lei Li, Xiaowu Sun, Yasmina Al Khalil, Di Liu, Sana Jabbar, Sandro Queirós, Francesco Galati, Moona Mazher, Zheyao Gao, Marcel Beetz, Lennart Tautz, Christoforos Galazis, Marta Varela, Markus Hullebrand, Vicente Grau, Xiahai Zhuang, Domenec Puig, Maria A. Zuluaga, Hassan Mohy-ud-Din, Dimitris Metaxas, Marcel Breeuwer, Rob J. van der Geest, Michelle Noga, Stephanie Bricq, Mark E. Rentschler, Andrea Guala, Steffen E. Petersen, Sergio Escalera, José F. Rodríguez Palomares, Karim Lekadir
Publikováno v:
Martin-Isla, C, Campello, V M, Izquierdo, C, Kushibar, K, Sendra-Balcells, C, Gkontra, P, Sojoudi, A, Fulton, M J, Arega, T W, Punithakumar, K, Li, L, Sun, X, Khalil, Y A, Liu, D, Jabbar, S, Queiros, S, Galati, F, Mazher, M, Gao, Z, Beetz, M, Tautz, L, Galazis, C, Varela, M, Hullebrand, M, Grau, V, Zhuang, X, Puig, D, Zuluaga, M A, Mohy-ud-Din, H, Metaxas, D, Breeuwer, M, Geest, R J V D, Noga, M, Bricq, S, Rentschler, M E, Guala, A, Petersen, S E, Escalera, S, Palomares, J F R & Lekadir, K 2023, ' Deep Learning Segmentation of the Right Ventricle in Cardiac MRI : The M &Ms challenge ', IEEE Journal of Biomedical and Health Informatics, pp. 1-14 . https://doi.org/10.1109/JBHI.2023.3267857
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32a6c9c7985326e535eda2261dc13b51
https://vbn.aau.dk/da/publications/12cc1dcd-8442-48eb-99a4-807c7dd00d98
https://vbn.aau.dk/da/publications/12cc1dcd-8442-48eb-99a4-807c7dd00d98
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031317774
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::93cd200008bbc6d269c861ab380c4751
https://doi.org/10.1007/978-3-031-31778-1_13
https://doi.org/10.1007/978-3-031-31778-1_13
Autor:
Tewodros Weldebirhan Arega, Stéphanie Bricq, François Legrand, Alexis Jacquier, Alain Lalande, Fabrice Meriaudeau
Publikováno v:
Medical Image Analysis. 86:102773
Autor:
Meriaudeau, Khawla Brahim, Tewodros Weldebirhan Arega, Arnaud Boucher, Stephanie Bricq, Anis Sakly, Fabrice
Publikováno v:
Sensors; Volume 22; Issue 6; Pages: 2084
Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classifica
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030937218
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::48106a4bdc65b8592eca5827f3d2e3b1
https://doi.org/10.1007/978-3-030-93722-5_27
https://doi.org/10.1007/978-3-030-93722-5_27
Publikováno v:
Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers ISBN: 9783031234422
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9898e730cb4d86ad44685eeee92797db
https://doi.org/10.1007/978-3-031-23443-9_39
https://doi.org/10.1007/978-3-031-23443-9_39
Autor:
Khawla, Brahim, Tewodros Weldebirhan, Arega, Arnaud, Boucher, Stephanie, Bricq, Anis, Sakly, Fabrice, Meriaudeau
Publikováno v:
Sensors (Basel, Switzerland). 22(6)
Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classifica
Publikováno v:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis ISBN: 9783030877347
UNSURE/PIPPI@MICCAI
UNSURE/PIPPI@MICCAI
In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b799d0e054b5386c3c52922dddf76eb5
https://doi.org/10.1007/978-3-030-87735-4_3
https://doi.org/10.1007/978-3-030-87735-4_3