针对血管分割难题，提出高效多提示框架VesSAM
VesSAM: Efficient Multi-Prompting for Segmenting Complex Vessel
- 用卷积适配器增强局部纹理，融合骨架等解剖提示
- 在8个数据集上提升Dice超10%，参数量远少于全微调方法
- 适合医疗影像中复杂血管的精准分割任务
准确的血管分割对疾病诊断和手术规划至关重要，但因血管细、分支多且纹理对比度低而困难。尽管基础模型如分割任意模型（SAM）在通用分割中表现良好，但在血管结构上效果不佳。本文提出VesSAM，一种专为2D血管分割设计的强大且高效的框架。该框架包含：(1) 卷积适配器以增强局部纹理特征；(2) 多提示编码器，通过分层交叉注意力融合解剖提示（包括骨架、分叉点、段中点）；(3) 轻量级掩码解码器以减少锯齿伪影。我们还构建了自动化多提示标注流程，并整理了一个涵盖8个数据集、5种成像模态的多样化基准数据集。实验表明，VesSAM在所有测试数据集上均显著优于现有基于参数高效微调（PEFT）的SAM变体，平均Dice提升超过10%，IoU提升13%，性能媲美全微调方法，但参数量极少。此外，其在分布外（OoD）设置下也表现出色，平均OoD Dice和IoU均优于所有基线。
原文摘要 · Abstract (English)
Accurate vessel segmentation is critical for clinical applications such as disease diagnosis and surgical planning, yet remains challenging due to thin, branching structures and low texture contrast. While foundation models like the Segment Anything Model (SAM) have shown promise in generic segmentation, they perform sub-optimally on vascular structures. In this work, we present VesSAM, a powerful and efficient framework tailored for 2D vessel segmentation. VesSAM integrates (1) a convolutional adapter to enhance local texture features, (2) a multi-prompt encoder that fuses anatomical prompts, including skeletons, bifurcation points, and segment midpoints, via hierarchical cross-attention, and (3) a lightweight mask decoder to reduce jagged artifacts. We also introduce an automated pipeline to generate structured multi-prompt annotations, and curate a diverse benchmark dataset spanning 8 datasets across 5 imaging modalities. Experimental results demonstrate that VesSAM consistently outperforms state-of-the-art PEFT-based SAM variants by over 10% Dice and 13% IoU, and achieves competitive performance compared to fully fine-tuned methods, with significantly fewer parameters. VesSAM also generalizes well to out-of-distribution (OoD) settings, outperforming all baselines in average OoD Dice and IoU.
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