Abstract: Medical image segmentation is critical for clinical diagnosis, but the scarcity of annotated data limits robust model training, making few-shot learning indispensable. Existing methods often suffer from two issues—performance degradation due to significant inter-class variations in pathological structures, and overreliance on attention mechanisms with high computational complexity (O(n²)), which hinders the efficient modeling of long-range dependencies. In contrast, the state space model (SSM) offers linear complexity (O(n)) and superior efficiency, making it a key solution. To address these challenges, we propose PPFFR (parallel prototype filter and feature refinement) for few-shot medical image segmentation. The proposed framework comprises three key modules. First, we propose the prototype refinement (PR) module to construct refined class subgraphs from encoder-extracted features of both support and query images, which generates support prototypes with minimized inter-class variation. We then propose the parallel prototype filter (PPF) module to suppress background interference and enhance the correlation between support and query prototypes. Finally, we implement the feature refinement (FR) module to further enhance segmentation accuracy and accelerate model convergence with SSM’s robust long-range dependency modeling capability, integrated with multi-head attention (MHA) to preserve spatial details. Experimental results on the Abd-MRI dataset demonstrate that FR with MHA outperforms FR alone in segmenting the left kidney, right kidney, liver, and spleen, and in terms of mean accuracy, confirming MHA’s role in improving precision. In extensive experiments conducted on three public datasets under the 1-way 1-shot setting, PPFFR achieves Dice scores of 87.62%, 86.74%, and 79.71% separately, consistently surpassing state-of-the-art few-shot medical image segmentation methods. As the critical component, SSM ensures that PPFFR balances performance with efficiency. Ablation studies validate the effectiveness of the PR, PPF, and FR modules. The results indicate that explicit inter-class variation reduction and SSM-based feature refinement can enhance accuracy without heavy computational overhead. In conclusion, PPFFR effectively enhances inter-class consistency and computational efficiency for few-shot medical image segmentation. This work provides insights for few-shot learning in medical imaging and inspires lightweight architecture designs for clinical deployment.

Key words: Few-shot learning; Medical image segmentation; Prototype filter; State space model

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