CASNet: curvature-aware cardiac MRI segmentation with multi-scale and attention-driven encoding for enhanced risk-oriented structural analysis

Accurate segmentation of cardiac structures in magnetic resonance imaging (MRI) is essential for reliable diagnosis and quantitative analysis of cardiovascular diseases. However, conventional convolutional neural networks often struggle to maintain both semantic consistency and geometric smoothness, particularly in challenging slices with high anatomical variability. In this work, we propose CASNet, a novel U-Net-based architecture that integrates three key enhancements to address these limitations. First, we introduce a Multi-Scale Context Block (MSCB) at the network bottleneck to enrich encoder features with diverse receptive fields, enabling robust representation of cardiac structures across varying spatial scales. Second, we replace standard skip connections with Cross-Attentive Skip Connections (CASC), allowing the decoder to selectively aggregate spatial features from encoder layers via attention-weighted fusion. This mitigates semantic mismatch and promotes more effective feature reuse. Third, we incorporate a Curvature-Aware Loss that penalizes second-order spatial discontinuities in the predicted segmentation, thereby improving the smoothness and anatomical plausibility of the boundaries. Extensive experiments on the ACDC dataset demonstrate that CASNet outperforms baseline U-Net models and recent attention-based architectures, achieving superior performance in both region overlap and boundary accuracy metrics. The proposed approach provides a robust and generalizable solution for high-precision cardiac MRI segmentation, which may serve as a foundation for future downstream clinical applications in AI-assisted cardiac analysis.