Abstract
Recent advances in state space models (SSMs) have demonstrated remarkable efficiency in modeling long-range dependencies, yet their application to 3D medical image segmentation remains underexplored. This paper introduces CDA-Mamba (Cross-Directional Attention Mamba), a novel hybrid architecture that combines the efficiency of SSMs with the strengths of convolutional and attention mechanisms to address the unique challenges of 3D medical image segmentation. CDA-Mamba features three key innovations: a Multi-Frequency Gated Convolution (MFGC) module to enhance spatial and frequency-domain feature integration, a Tri-Directional Mamba module to capture volumetric dependencies across orthogonal dimensions, and Selective Self-Attention integration in high-semantic layers to balance computational efficiency with global context modeling. Comprehensive experiments on the BraTS2023 brain tumor segmentation dataset highlight the competitive performance of CDA-Mamba, which achieves an average Dice score of 91.44. Moreover, evaluations on the AIIB2023 airway segmentation dataset further validate its effectiveness, with CDA-Mamba attaining the highest IoU of 88.72 and a DLR of 71.01. These results underscore its ability to balance accuracy and efficiency in 3D medical image segmentation.