Evaluation of deep learning-based myocardial infarction quantification using Segment CMR software
This work addresses the problem of automating myocardial infarction quantification for clinicians, potentially improving efficiency in cardiac MRI analysis.
This paper evaluates a deep learning-based method for quantifying myocardial infarction (MI) using Segment CMR software. The study compares the results of the U-net based semantic segmentation with those from medical experts to estimate the relationship between the two quantification approaches.
This work evaluates deep learning-based myocardial infarction (MI) quantification using Segment cardiovascular magnetic resonance (CMR) software. Segment CMR software incorporates the expectation-maximization, weighted intensity, a priori information (EWA) algorithm used to generate the infarct scar volume, infarct scar percentage, and microvascular obstruction percentage. Here, Segment CMR software segmentation algorithm is updated with semantic segmentation with U-net to achieve and evaluate fully automated or deep learning-based MI quantification. The direct observation of graphs and the number of infarcted and contoured myocardium are two options used to estimate the relationship between deep learning-based MI quantification and medical expert-based results.