An End-to-End Cardiac MRI Segmentation and Multi-Class Pathology Classification Pipeline Using Ensemble U-Net Models and Explainable Machine Learning

Accurate delineation of cardiac structures in cardiac magnetic resonance (CMR) images is a prerequisite for quantifying cardiac function. This paper presents an end-to-end pipeline for automated cardiac segmentation and five-class pathology classification, evaluated on the 150 patients of the Automated Cardiac Diagnosis Challenge (ACDC) dataset. Three U-Net-based networks were trained with a hybrid cross-entropy-Dice loss selected through an ablation study and combined through soft-voting and weighted ensembles. On the official test set, the weighted ensemble achieved a mean Dice score of 90.27% at the slice level and 86.23% in volumetric evaluation, matching the best individual network and significantly outperforming the other two. Biomarkers extracted from the ensemble masks agreed closely with ground-truth-derived values (left ventricular ejection fraction: intraclass correlation 0.975) and were used to train nine machine learning classifiers under nested cross-validation. The four best classifiers achieved macro F1 scores between 88.73 and 89.14%. A cross-source evaluation across five segmentation sources showed no significant difference between classification based on automatically derived biomarkers and on ground-truth-derived biomarkers. SHAP analysis identified class-specific biomarkers consistent with cardiac pathophysiology. The results demonstrate technical feasibility on a public benchmark; external multi-center validation is required before clinical use.