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Contrast-Free Myocardial Infarction Segmentation with Attention U-Net
Khaled Ali Deeb1, Yasmeen Alshelle2, Hala Hammoud2
1Department of Information Processing and Management Systems, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Insights
This study introduces a deep learning framework for automatic cardiac segmentation and myocardial infarction detection using non-contrast cardiovascular magnetic resonance imaging, improving efficiency and accessibility.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Cardiovascular magnetic resonance (CMR) is the gold standard for cardiac assessment but manual segmentation is time-consuming and variable.
- Deep learning (DL) automates segmentation, yet struggles with generalizability in non-contrast cine CMR for myocardial infarction (MI) detection.
Purpose of the Study:
- To develop a DL framework for automated cardiac structure and MI segmentation using contrast-free cine CMR.
- To enhance the clinical applicability of CMR by enabling contrast-independent cardiac assessment.
Main Methods:
- Integrated multiple CNNs for cardiac structure segmentation and an attention-based DL model for MI localization.
- Employed post-processing with stacked autoencoders and active contour modeling for anatomical consistency.
- Evaluated performance using Dice Similarity Coefficient (DSC), Mean Contour Distance (MCD), and Hausdorff Distance (HD).
Main Results:
- Achieved high Dice scores: 0.93 for LV cavity, 0.89 for LV myocardium, 0.91 for RV cavity.
- Demonstrated reliable MI segmentation with a Dice score of 0.80 and high recall.
- Showcased consistently low boundary errors across all segmented cardiac structures.
Conclusions:
- The proposed DL framework enables accurate, contrast-free segmentation of cardiac structures and MI from cine CMR.
- Facilitates broader clinical use, especially for patients with contrast contraindications or in resource-limited settings.
- Supports scalable and reliable cardiac assessment independent of contrast agents.
Abstract:
Background: Cardiovascular magnetic resonance (CMR) is the clinical gold standard for assessing cardiac anatomy and function. However, the manual segmentation of cardiac structures and myocardial infarction (MI) is time-consuming, prone to inter-observer variability, and often depends on contrast-enhanced imaging. Although deep learning (DL) has enabled substantial automation, challenges remain in generalizability, particularly for MI detection from non-contrast cine CMR. Objective: This study proposes a comprehensive DL-based framework for automatic segmentation of cardiac structures and myocardial infarction using contrast-free cine CMR. Methods: The framework integrates multiple convolutional neural network (CNN) architectures for cardiac structure segmentation with an attention-based deep learning model for MI localization. Post-processing refinement using stacked autoencoders and active contour modeling is applied to improve anatomical consistency. Segmentation performance is evaluated using overlap-based and boundary-based metrics, including the Dice Similarity Coefficient (DSC), Mean Contour Distance (MCD), and Hausdorff Distance (HD). Results: The best-performing model achieved Dice scores of 0.93 ± 0.05 for the left ventricular (LV) cavity, 0.89 ± 0.04 for the LV myocardium, and 0.91 ± 0.06 for the right ventricular (RV) cavity, with consistently low boundary errors across all structures. Myocardial infarction segmentation achieved a Dice score of 0.80 ± 0.02 with high recall, demonstrating reliable infarct localization without the use of contrast agents. Conclusions: By enabling accurate cardiac structure and myocardial infarction segmentation from contrast-free cine CMR, the proposed framework supports broader clinical applicability, particularly for patients with contraindications to gadolinium-based contrast agents and in emergency or resource-limited settings. This approach facilitates scalable, contrast-independent cardiac assessment.
