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

•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...

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Veröffentlicht in:	Medical image analysis 2022-07, Vol.79, p.102428-102428, Article 102428
Hauptverfasser:	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:	Artikel
Sprache:	eng
Schlagworte:	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
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