CAMUS-HeartNet: A Deep Meta-Ensemble Architecture for Accurate Cardiac Tissue Segmentation

Cardiac MRI segmentation remains a critical yet challenging task in medical image analysis, particularly for accurate delineation of multi-class cardiac structures using standard public datasets like CAMUS. In this work, we introduce CAMUS-HeartNet, a deep meta-ensemble architecture combining multiple U-Net variants with a meta-learner that intelligently fuses their predictions. We rigorously evaluate our method on the CAMUS dataset and achieve global mean Dice = 0.9298 and overall pixel accuracy = 96.80 %, surpassing many existing models applied to this dataset. Class-wise Dice scores -- Background: 0.9861, LV: 0.9424, Myocardium: 0.8792, RV: 0.9115 -- attest to the models strength even in challenging myocardial boundaries. AUC values exceed 0.99 for all classes, indicating exceptional discrimination capacity. To the best of our knowledge, no prior study on CAMUS has reported consistently such high performance across all cardiac structures simultaneously with a meta-ensemble strategy. This work demonstrates that meta-learner guided ensembling can push the frontier of automated cardiac tissue segmentation, offering a robust and accurate tool for downstream clinical and research applications.