<p>Accurate segmentation of cardiac structures remains a challenging task in cardiac image analysis, particularly due to complex anatomical variations and dynamic, nonlinear deformations across the cardiac cycle. Traditional approaches often struggle to maintain precision under these conditions, especially when faced with ambiguous boundaries and low-contrast regions in cardiac magnetic resonance imaging (MRI). To handle this, we explore the hierarchical nature of cardiac deformations while preserving structural boundaries and propose DABC-Net (Deformation Aggregation Boundary-Constrained Network), a novel framework that integrates hierarchical deformation feature aggregation with boundary-aware supervision to enhance cardiac MRI segmentation. To effectively encode the spatially varying deformation patterns, we design SDIM (Shape Deformation Integration Module) that progressively models multi-level anatomical deformations, enabling the network to adaptively learn deformation-aware representations across different feature stages. Complementarily, a DBAM (Dual-Branch Adaptive Mixer) is introduced to bridge fine-grained feature and broad contextual semantics, promoting robust alignment in the presence of complex shape dynamics and intensity inconsistencies. To further address the challenge of precise boundary localization, we incorporate a Boundary-Constrained Supervision Strategy, which guides the network to focus on fine structural details through both architectural and loss-based refinements. Extensive experiments on publicly available MM-WHS and ACDC datasets demonstrate that DABC-Net consistently achieves high segmentation accuracy and robust generalization across different cardiac pathologies. Our results underscore the effectiveness of combining hierarchical deformation modeling with structural boundary constraints, achieving state-of-the-art performance while advancing anatomical understanding in cardiac image segmentation.</p>