# MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

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## Abstract

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## 1. Introduction

## 2. Materials

## 3. Automated Segmentation of Myocardial Infarction: Myocardial Infarction-Net (MyI-NET)

#### 3.1. Feature Extraction via MI-ResNet

_{l}is the output of the l-th layer in the conventional CNN’s counterpart, and ${R}_{l}$ is the output of a ResNet constructed from the original CNN by adding a short-cut connection (residual information).

#### 3.2. Feature Extraction via MI-MobileNet

#### 3.3. Atrous Spatial Pyramid Pooling

#### 3.4. Weight Matrix

#### 3.5. Data Augmentation

#### 3.6. Performance Metrics

## 4. Experiments and Results

#### 4.1. Data Preparation

#### 4.2. Experiment Environment

#### 4.3. Segmentation Result Based of Proposed Method

#### 4.4. Segmentation Result Based on State of Art Methods

## 5. Conclusions

#### 5.1. A Short Summary of Results

#### 5.2. Limitations and Directions for Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 6.**Residual block and down-sizing block in MobileNetV2. (

**a**) Residual block (Stride = 1) (

**b**) Down-sizing block (Stride = 2).

**Figure 7.**Atrous convolution (the red dot means non-zero). (

**a**) Conventional convolution. (

**b**) Dilation convolution(k = 2). (

**c**) Dilation convolution(k = 3).

**Figure 10.**Examples of data augmentation. (

**a**) Random rotation within [0, 360]. (

**b**) Random scaling within [0.9, 1.1].

**Figure 13.**Bar scatter plot of the performance based on per image of MI-ResNet18-AC, UNet vs MI-ResNet50-AC. (

**a**) Global accuracy. (

**b**) Mean accuracy. (

**c**) Mean accuracy.

**Figure 16.**Detection result per case. Series 1 includes false alarms and series 2 only contains the true positives.

Variables | Values | Number | Rate |
---|---|---|---|

Stemi | 52 | 17.28% | |

Non-stemi | 249 | 82.72% | |

Gender | Male | 172 | 57.14% |

Female | 129 | 42.86% | |

Age at MRI | 90–99 | 3 | 1% |

80–89 | 11 | 3.65% | |

70–79 | 44 | 14.62% | |

60–69 | 77 | 25.58% | |

50–59 | 74 | 24.58% | |

49–49 | 56 | 18.60% | |

30–39 | 35 | 11.63% | |

20–29 | 1 | 0.33% | |

Average | 57 | Std | 13.67 |

Name | Parameters |
---|---|

Training algorithm | Data |

Learn rate drop period | 10 |

Learn rate drop factors | 3 |

Initial learn rate | ${e}^{-3}$ |

Max epochs | 50 |

Mini batch size | 10 |

Execute environment | GPU |

Validation patience | 4 |

Type | Weight |
---|---|

Training algorithm | SGDM |

Learn rate drop period | 10 |

Learn rate drop factors | 3 |

Initial learn rate | ${e}^{-3}$ |

Max epochs | 50 |

Mini batch size | 10 |

Execute environment | GPU |

Validation patience | 4 |

Model | Global Accuracy | Mean Accuracy | wIoU | Bfscore |
---|---|---|---|---|

MI-MobileNet-AC | 0.9569 | 0.8202 | 0.9463 | 0.5351 |

MI-ResNet50-AC | 0.9738 | 0.8601 | 0.9647 | 0.6446 |

MI-ResNet18-AC | 0.9679 | 0.8483 | 0.9584 | 0.5839 |

Category | LGE | Blood | Muscle | Background | |
---|---|---|---|---|---|

MI-ResNet50-AC | Accuracy | 0.6429 | 0.8402 | 0.8779 | 0.9686 |

bfscore | 0.4634 | 0.6837 | 0.4022 | 0.8552 | |

MI-ResNet18-AC | Accuracy | 0.7441 | 0.8255 | 0.8511 | 0.9724 |

bfscore | 0.4221 | 0.6226 | 0.4187 | 0.8559 | |

MI-MobileNet-AC | Accuracy | 0.4245 | 0.8809 | 0.8567 | 0.9664 |

bfscore | 0.3669 | 0.5996 | 0.3729 | 0.8411 |

Target Class | |||||
---|---|---|---|---|---|

LGE | Blood | Muscle | Background | ||

MI-ResNet50-AC Output class | LGE | 0.6429 | 0.1557 | 0.1982 | 0.0031 |

Blood | 0.0543 | 0.8402 | 0.0980 | 0.0074 | |

Muscle | 0.0352 | 0.0640 | 0.8779 | 0.0229 | |

Background | 0 | 0.0016 | 0.0291 | 0.9686 | |

MI-ResNet18-AC Output class | LGE | 0.7441 | 0.1180 | 0.1371 | 0 |

Blood | 0.0774 | 0.8255 | 0.0904 | 0.0068 | |

Muscle | 0.0676 | 0.0621 | 0.8511 | 0.0193 | |

Background | 0.0023 | 0.0023 | 0.0230 | 0.9724 | |

MI-MobileNet-AC Output class | LGE | 0.4245 | 0.3429 | 0.2326 | 0 |

Blood | 0.0242 | 0.8809 | 0.0870 | 0.0079 | |

Muscle | 0.0266 | 0.0964 | 0.8567 | 0.0203 | |

Background | 0.0011 | 0.0051 | 0.0273 | 0.9664 |

Model | Training Time Cost |
---|---|

MI-MobileNet-AC | 24′1″ |

MI-ReNet50-AC | 57′35″ |

MI-ResNet18-AC | 24′50″ |

Model | Global Accuracy | Mean Accuracy | wIoU | Bfscore |
---|---|---|---|---|

CNN | 0.6021 | 0.5632 | 0.4367 | 0.1574 |

MI-ResNet50-AC | 0.9738 | 0.8601 | 0.9647 | 0.6446 |

Unet | 0.6332 | 0.6222 | 0.6117 | 0.1626 |

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## Share and Cite

**MDPI and ACS Style**

Wang, S.; Abdelaty, A.M.S.E.K.; Parke, K.; Arnold, J.R.; McCann, G.P.; Tyukin, I.Y.
MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images. *Entropy* **2023**, *25*, 431.
https://doi.org/10.3390/e25030431

**AMA Style**

Wang S, Abdelaty AMSEK, Parke K, Arnold JR, McCann GP, Tyukin IY.
MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images. *Entropy*. 2023; 25(3):431.
https://doi.org/10.3390/e25030431

**Chicago/Turabian Style**

Wang, Shuihua, Ahmed M. S. E. K. Abdelaty, Kelly Parke, Jayanth Ranjit Arnold, Gerry P. McCann, and Ivan Y. Tyukin.
2023. "MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images" *Entropy* 25, no. 3: 431.
https://doi.org/10.3390/e25030431