Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
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by
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 (+21 others)
2021
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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