# Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm

## TL;DR

This paper introduces an end-to-end deep learning framework called OF-RNN for precise pixel-level detection of myocardial infarction areas in cardiac MRI images, aiding early diagnosis and management.

## Contribution

The study presents a novel deep learning architecture integrating localization, motion analysis, and classification layers for accurate MI detection at the pixel level.

## Key findings

- Achieved 94.35% accuracy in MI pixel classification
- Effectively characterized myocardial physiologic function
- Demonstrated potential for standardized MI assessment

## Abstract

Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management. In this study, we propose an end-to-end deep-learning algorithm framework (OF-RNN ) to accurately detect the MI area at the pixel level. Our OF-RNN consists of three different function layers: the heart localization layers, which can accurately and automatically crop the region-of-interest (ROI) sequences, including the left ventricle, using the whole cardiac magnetic resonance image sequences; the motion statistical layers, which are used to build a time-series architecture to capture two types of motion features (at the pixel-level) by integrating the local motion features generated by long short-term memory-recurrent neural networks and the global motion features generated by deep optical flows from the whole ROI sequence, which can effectively characterize myocardial physiologic function; and the fully connected discriminate layers, which use stacked auto-encoders to further learn these features, and they use a softmax classifier to build the correspondences from the motion features to the tissue identities (infarction or not) for each pixel. Through the seamless connection of each layer, our OF-RNN can obtain the area, position, and shape of the MI for each patient. Our proposed framework yielded an overall classification accuracy of 94.35% at the pixel level, from 114 clinical subjects. These results indicate the potential of our proposed method in aiding standardized MI assessments.