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

• Published in: Medical image analysis Vol. 79; p. 102428
• Main Authors: Lalande, Alain, Chen, Zhihao, Pommier, Thibaut, Decourselle, Thomas, Qayyum, Abdul, Salomon, Michel, Ginhac, Dominique, Skandarani, Youssef, Boucher, Arnaud, Brahim, Khawla, de Bruijne, Marleen, Camarasa, Robin, Correia, Teresa M., Feng, Xue, Girum, Kibrom B., Hennemuth, Anja, Huellebrand, Markus, Hussain, Raabid, Ivantsits, Matthias, Ma, Jun, Meyer, Craig, Sharma, Rishabh, Shi, Jixi, Tsekos, Nikolaos V., Varela, Marta, Wang, Xiyue, Yang, Sen, Zhang, Hannu, Zhang, Yichi, Zhou, Yuncheng, Zhuang, Xiahai, Couturier, Raphael, Meriaudeau, Fabrice
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2022; Elsevier BV; Elsevier
• Subjects: CNN; Computer Science; Contrast agents; DE-MRI; Deep learning; Emergency medical care; Evaluation; Heart attacks; Infarction; Injection; Machine learning; Magnetic resonance imaging; Medical Imaging; Myocardial infarction; Myocardium; Reperfusion; Segmentation; Teaching methods; Myocardium; CNN; Infarction; DE-MRI

•Dataset of 150 DE-MRI exams in short-axis orientation with the manual drawing.•The used dataset include clinical information that could be recorded in emergency department in addition to the MR images.•The first objective is to compare the latest methodological developments in image processing to s...