EccoMamba: Enhanced Cross-hierarchical Continuity Orthogonal Mamba for Medical Image Segmentation

Medical image segmentation plays a critical role in clinical diagnosis, lesion quantification, and preoperative planning. However, conventional Mamba-based architectures, which rely on fixed-directional sequence modeling and flattening of images into one-dimensional sequences, fail to effectively capture hierarchical anatomical features and spatial dependencies, limiting their representation capacity for complex medical structures. To address these challenges, we propose EccoMamba (Enhanced Cross-hierarchical Continuity Orthogonal Mamba), a U-shaped encoder-decoder framework tailored for medical image segmentation. In the encoder’s downsampling path, we introduce the hierarchical aggregation enhancement (HAE) module, which integrates multi-scale convolutions with hierarchical attention. The attention branch further incorporates cross-channel interactions, enabling the model to amplify semantically relevant features and suppress background noise selectively. For skip connections, we design the structural continuity orthogonal (SCO) module to maintain spatial continuity by modeling cross-dimensional dependencies via orthogonal axial shifts, mitigating directional bias and enhancing anatomical consistency. Extensive experiments on four benchmark datasets, ISIC 2018, ISIC 2017, Synapse, and ACDC, demonstrate that EccoMamba consistently outperforms state-of-the-art methods in both segmentation accuracy and structural fidelity. The code is available at https://anonymous.4open.science/r/EccoMamba-4117/.