Inter-Slice Dual Cross Attention and Class-Level Alignment for 2.5D Medical Image Segmentation

Capturing contextual information across adjacent slices is critical for accurate medical image segmentation. While 2D methods are computationally efficient, they often fail to model inter-slice continuity. In contrast, 3D methods capture volumetric context but require substantial computational resources. To overcome these limitations, we propose a 2.5D segmentation framework that incorporates inter-slice context while maintaining relatively high efficiency. We introduce a Dual Cross Attention (DCA) module that captures both global spatial and channel dependencies across adjacent slices. Furthermore, to enhance inter-slice consistency, we employ class-wise prototype learning, which aligns pixel embeddings of the same class across adjacent slices. Additionally, we introduce a semantic correlation loss to align DCA-refined features with decoder predictions via cosine similarity, guiding each channel to correspond more semantically with its associated class. Experiments conducted on the FLARE22 and MM-WHS datasets demonstrate that our method outperforms both 2D and 2.5D baselines, validating the effectiveness of modeling inter-slice dependencies and promoting class-level representation alignment in medical image segmentation.