Accurate delineation of cardiac structures from cine magnetic resonance imaging (MRI) is essential for quantitative assessment of ventricular function and for diagnosing cardiomyopathies. Convolutional encoders, although highly effective, capture context within a limited receptive field and may underrepresent the long-range spatial dependencies that characterize the heart across the cardiac cycle. In this work, we present a fully supervised framework that couples a Vision Transformer (ViT) encoder with a convolutional decoder to segment the left ventricle (LV), right ventricle (RV), and myocardium (Myo) on the Automated Cardiac Diagnosis Challenge (ACDC) dataset. The transformer backbone mo... More >