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

arXiv: https://arxiv.org/abs/2012.09070 · HTML: https://mlsanity.com/paper/2012.09070/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Evaluation of Extra Pixel Interpolation with Mask Processing for Medical Image Segmentation with Deep Learning](https://mlsanity.com/paper/2302.11522/) (2023)

## Abstract (arXiv)

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.