Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

Alain Lalande (1) , Zhihao Chen (2) , Thibaut Pommier (3) , Thomas Decourselle (4) , Abdul Qayyum (1) , Michel Salomon (2) , Dominique Ginhac (5) , Youssef Skandarani (1) , Arnaud Boucher (1) , Khawla Brahim (1) , Marleen de Bruijne , Robin Camarasa , Teresa Correia , Xue Feng , Kibrom Girum (1) , Anja Hennemuth , Markus Huellebrand , Raabid Hussain (1) , Matthias Ivantsits , Jun Ma , Craig Meyer , Rishabh Sharma , Jixi Shi , Nikolaos Tsekos , Marta Varela , Xiyue Wang , Sen Yang , Hannu Zhang , Yichi Zhang , Yuncheng Zhou , Xiahai Zhuang , Raphael Couturier (2) , Fabrice Meriaudeau (1)

1 Equipe IFTIM [ImViA - EA7535]
2 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
3 Service de Cardiologie [CHU de Dijon]
4 CASIS - CArdiac Simulation and Imaging Software
5 CORES - Equipe CORES [ImViA - EA7535]

Domaines: Imagerie médicale

Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, et al.. Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge. Medical Image Analysis, 2022, 79, pp.102428. ⟨10.1016/j.media.2022.102428⟩. ⟨hal-03682606⟩