A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.

翻译：心肌梗塞后心脏状态评估的一个关键因素是测量心肌梗塞后心肌部分是否可行。 心肌梗塞在再融合或再血管化治疗后是否可行。 首先, 评估深度学习方法能否区分正常和病理病例。 其次, 自动计算心肌梗塞的程度。 公开数据库包括150次检查,在注射对比剂后分为50次,正常心肌梗塞,正常为50次,而正常的心肌梗塞,因此是评价心肌梗塞程度的一种选择方法。 为了自动评估心肌梗塞状况,本文介绍了EMIDEC挑战中侧重于这项任务的结果。 与MRI一道, 临床特征也提供了两个方面的目标。 从若干工程中获得的结果, 提供了最佳的心肌梗解程度, 也提供了一种自动的心肌梗塞程度, 一种自动的心肌梗塞的分类方法, 提供了一种最佳的心肌部位。