Evaluation of deep learning-based myocardial infarction quantification using Segment CMR software

• URL: http://arxiv.org/abs/2012.09070v1
• Date: Wed, 16 Dec 2020 16:49:50 GMT
• Title: Evaluation of deep learning-based myocardial infarction quantification using Segment CMR software
• Authors: Olivier Rukundo
• Abstract summary: The author evaluates the preliminary work related to automating the quantification of the size of the myocardial infarction (MI) using deep learning in Segment cardiovascular magnetic resonance (CMR) software. Experimental evaluation of the size of the MI shows that more than 50 % (average infarct scar volume), 75% (average infarct scar percentage), and 65 % (average microvascular obstruction percentage) of the network-based results are approximately very close to the expert delineation-based results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, the author evaluates the preliminary work related to automating the quantification of the size of the myocardial infarction (MI) using deep learning in Segment cardiovascular magnetic resonance (CMR) software. Here, deep learning is used to automate the segmentation of myocardial boundaries before triggering the automatic quantification of the size of the MI using the expectation-maximization, weighted intensity, a priori information (EWA) algorithm incorporated in the Segment CMR software. Experimental evaluation of the size of the MI shows that more than 50 % (average infarct scar volume), 75% (average infarct scar percentage), and 65 % (average microvascular obstruction percentage) of the network-based results are approximately very close to the expert delineation-based results. Also, in an experiment involving the visualization of myocardial and infarct contours, in all images of the selected stack, the network and expert-based results tie in terms of the number of infarcted and contoured images.

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