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Accurate segmentation of ventricular structures and the myocardium from Cardiac Magnetic Resonance (CMR) images is essential for the diagnosis and management of cardiovascular diseases. This study presents a comprehensive approach to cardiac MRI segmentation by developing and evaluating six U-Net variants: Original U-Net, Residual U-Net, Attention U-Net, Feature Pyramid U-Net, Feedback Residual U-Net, and Transformer-Based U-Net, each incorporating architectural enhancements tailored to address specific challenges in segmenting complex cardiac anatomy. These architectures incorporate advanced enhancements such as deeper encoder levels, attention mechanisms, residual connections, multi-scale feature fusion, transformer modules, and feedback mechanisms. To improve segmentation robustness, a novel hybrid loss function, combining Dice Loss and Cross-Entropy Loss, was proposed to effectively manage class imbalance and improve segmentation precision. Among the evaluated models, the Feature Pyramid U-Net achieved the highest performance, with Dice coefficients of 0.9388 (Left Ventricle), 0.8759 (Right Ventricle), and 0.8426 (Myocardium), demonstrating its superior ability to capture multi-scale contextual information. To bridge the gap between research and clinical application, an interactive web application was developed and deployed, enabling real-time inference, visual inspection of annotated segmentation, and region-specific descriptions through a user-friendly interface. This work not only advances the design of deep learning architectures for medical image segmentation, but also demonstrates a practical pathway for integrating these models into clinical workflows.
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