MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images
Abstract
:1. Introduction
2. Materials
3. Automated Segmentation of Myocardial Infarction: Myocardial Infarction-Net (MyI-NET)
3.1. Feature Extraction via MI-ResNet
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
Finanzierung
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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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 | |
Max epochs | 50 |
Mini batch size | 10 |
Execute environment | GPU |
Validation patience | 4 |
Typ | Weight |
---|---|
Training algorithm | SGDM |
Learn rate drop period | 10 |
Learn rate drop factors | 3 |
Initial learn rate | |
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 |
Kategorie | 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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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
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 StyleWang, 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