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<p>The Segment Anything Model (SAM), a foundation model for promptable image segmentation, has shown strong performance on zero-shot natural image segmentation tasks. However, due to the significant differences between the natural and medical image domains, as well as the subtle lesions and blurred boundaries inherent in medical images, SAM suffers a marked performance drop when applied to medical images. To address these challenges, we propose DB-SAM, a domain-adaptive and boundary-enhanced framework for few-shot medical image segmentation. To better adapt to medical image segmentation, we fine-tune the image encoder using a hybrid feature representation that incorporates low-level features. During the fine-tuning process, a nonlinear mapping method is employed to update a small portion of weights, thereby capturing rich features in medical images. Furthermore, to supplement the multi-scale and boundary features required for medical image segmentation, a multi-scale feature extractor and a boundary-aware module are designed. The multi-scale feature extractor enhances the representation of local receptive field features, providing SAM with rich local multi-scale contextual information. The boundary-aware module leverages limited input data to capture enhanced boundary-representative features by encoding boundary and texture features. Experimental results on three datasets from different modalities demonstrate that our DB-SAM outperforms the state-of-the-arts methods, and remarkably, even with only one labeled training sample, it consistently surpasses various SAM and CNN-based methods. The code is publicly available at <a href="https://github.com/ZZY010411/DB-SAM">https://github.com/ZZY010411/DB-SAM</a>.</p>

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DB-SAM: Boundary-aware and domain-adaptive SAM for generalizable few-shot medical image segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

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  • Yaxue Wen,
  • Yuqin Li,
  • Ziyang Zhang,
  • Yu Miao,
  • WeiLi Shi,
  • ZhenGang Jiang

摘要

The Segment Anything Model (SAM), a foundation model for promptable image segmentation, has shown strong performance on zero-shot natural image segmentation tasks. However, due to the significant differences between the natural and medical image domains, as well as the subtle lesions and blurred boundaries inherent in medical images, SAM suffers a marked performance drop when applied to medical images. To address these challenges, we propose DB-SAM, a domain-adaptive and boundary-enhanced framework for few-shot medical image segmentation. To better adapt to medical image segmentation, we fine-tune the image encoder using a hybrid feature representation that incorporates low-level features. During the fine-tuning process, a nonlinear mapping method is employed to update a small portion of weights, thereby capturing rich features in medical images. Furthermore, to supplement the multi-scale and boundary features required for medical image segmentation, a multi-scale feature extractor and a boundary-aware module are designed. The multi-scale feature extractor enhances the representation of local receptive field features, providing SAM with rich local multi-scale contextual information. The boundary-aware module leverages limited input data to capture enhanced boundary-representative features by encoding boundary and texture features. Experimental results on three datasets from different modalities demonstrate that our DB-SAM outperforms the state-of-the-arts methods, and remarkably, even with only one labeled training sample, it consistently surpasses various SAM and CNN-based methods. The code is publicly available at https://github.com/ZZY010411/DB-SAM.