Towards Clinical-Grade Cardiac MRI Segmentation: An Ensemble of Improved UNet Architectures

Accurate cardiac MRI segmentation is essential for quantitative analysis of cardiac structure and function in clinical practice. In this study, we propose an ensemble framework combining several improved UNet-based architectures to achieve robust and clinically reliable segmentation performance. The ensemble integrates multiple models, including variants of standard UNet, Residual UNet, and Attention UNet, optimized through extensive hyperparameter tuning and data augmentation on the CAMUS subject-based dataset. Experimental results demonstrate that our approach achieves a Dice similarity coefficient of 0.91, surpassing several state-of-the-art methods reported in recent literature. Moreover, the proposed ensemble exhibits exceptional stability across subjects and maintains high generalization performance, indicating its strong potential for real-world clinical deployment. This work highlights the effectiveness of ensemble deep learning techniques for cardiac image segmentation and represents a promising step towards clinical-grade automated analysis in cardiac imaging.