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

TL;DR

This paper evaluates deep learning methods for automatic classification and segmentation of myocardial infarction in delayed enhancement-MRI, demonstrating high accuracy in classification but highlighting challenges in segmenting small infarct areas.

Contribution

It presents the results of the EMIDEC challenge, showcasing the potential of deep learning for automatic cardiac MRI analysis and identifying areas for improvement.

Findings

Classification accuracy up to 0.92 achieved

Automatic myocardium segmentation demonstrated

Infarct area segmentation remains challenging

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.