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<p>While deep learning has achieved significant progress in medical image segmentation, its reliance on large-scale annotated data remains a major bottleneck in limited-sample scenarios. Few-shot medical image segmentation (FSMIS) aims to address this challenge by enabling effective segmentation under data-scarce conditions. However, existing prototype-based methods typically generate prototypes from support features via random sampling or local averaging, thereby overlooking the query-specific demands. To this end, we propose ReLiFSS (Reliability-Aware Few-Shot Medical Image Segmentation), a query-feature-guided method. Its core idea is to directly integrate query features into the prototype generation process to construct customized prototypes tailored to different query images. Specifically, we design a Query Pseudo-Prototype Generation (QPPG) module, which constructs an initial prototype using support features and performs preliminary segmentation on the query image to extract a pseudo-prototype reflecting its specific requirements. Subsequently, the High-Confidence Support Prototype Generation (HSPG) module utilizes this pseudo-prototype to reverse-segment the support set, mining the feature regions discriminative for the query image segmentation. Finally, the Multiple Prototypes Matching Segmentation (MPMS) module fuses foreground and background information via a dual-pathway mechanism, helping alleviate foreground-background imbalance. Extensive experiments on three public medical image datasets show that our method achieves competitive performance compared with recent FSMIS methods. The code is available at: <a href="https://github.com/caihanyue49-art/ReLiFSS">https://github.com/caihanyue49-art/ReLiFSS</a>.</p>

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ReLiFSS: reliability-aware few-shot medical image segmentation via query pseudo-prototype and reverse support segmentation

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Bin Ge,
  • Caihan Yue,
  • Chenxing Xia,
  • Han Han,
  • Jialin Ma,
  • Zhikang Zhu

摘要

While deep learning has achieved significant progress in medical image segmentation, its reliance on large-scale annotated data remains a major bottleneck in limited-sample scenarios. Few-shot medical image segmentation (FSMIS) aims to address this challenge by enabling effective segmentation under data-scarce conditions. However, existing prototype-based methods typically generate prototypes from support features via random sampling or local averaging, thereby overlooking the query-specific demands. To this end, we propose ReLiFSS (Reliability-Aware Few-Shot Medical Image Segmentation), a query-feature-guided method. Its core idea is to directly integrate query features into the prototype generation process to construct customized prototypes tailored to different query images. Specifically, we design a Query Pseudo-Prototype Generation (QPPG) module, which constructs an initial prototype using support features and performs preliminary segmentation on the query image to extract a pseudo-prototype reflecting its specific requirements. Subsequently, the High-Confidence Support Prototype Generation (HSPG) module utilizes this pseudo-prototype to reverse-segment the support set, mining the feature regions discriminative for the query image segmentation. Finally, the Multiple Prototypes Matching Segmentation (MPMS) module fuses foreground and background information via a dual-pathway mechanism, helping alleviate foreground-background imbalance. Extensive experiments on three public medical image datasets show that our method achieves competitive performance compared with recent FSMIS methods. The code is available at: https://github.com/caihanyue49-art/ReLiFSS.