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

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

arXiv · 2108.04016

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

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Abstract

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.

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Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa M. Correia, Xue Feng, Kibrom B. Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma, Craig Meyer, Rishabh Sharma, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Xiyue Wang, Sen Yang, Hannu Zhang, Yichi Zhang, Yuncheng Zhou, Xiahai Zhuang, Raphael Couturier, Fabrice Meriaudeau. 2021-08-09. Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge. https://arxiv.org/abs/2108.04016

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