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

Olivier Rukundo

doi:10.1117/12.2601749

9 October 2021 Evaluation of deep-learning-based myocardial infarction quantification using Segment CMR software

Olivier Rukundo

Proceedings Volume 11897, Optoelectronic Imaging and Multimedia Technology VIII; 1189716 (2021) https://doi.org/10.1117/12.2601749
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Conference Poster

Abstract

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 expertbased results.

Citation Download Citation

Olivier Rukundo "Evaluation of deep-learning-based myocardial infarction quantification using Segment CMR software", Proc. SPIE 11897, Optoelectronic Imaging and Multimedia Technology VIII, 1189716 (9 October 2021); https://doi.org/10.1117/12.2601749

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