发表机构
Leiden University Medical Center（莱顿大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有方法难以实现跨模态通用血管分割的问题，提出基于SAM的ReG-SAM框架，利用参考图集生成图提示嵌入和血管原型嵌入，在19个数据集上超越基线，尤其擅长细薄血管分割。
AI 中文摘要
医学图像中的血管分割对于许多临床任务至关重要，从诊断到治疗规划均不可或缺。然而，由于复杂的血管形态和多样的成像条件，血管分割仍然具有挑战性。现有的深度学习方法很少致力于构建跨解剖部位和模态的通用血管分割器。尽管分割一切模型（SAM）在医学图像分割中展现出潜力，但其原始设计并未充分利用血管形态，且在精细血管结构上表现不佳，导致性能欠佳。本文提出了ReG-SAM，一种专为二维血管分割定制的基于SAM的框架，利用参考图集来增强血管表征。具体而言，我们从参考掩膜中引入两种模态感知表征：图提示嵌入（GPEs），用于编码来自图的全局空间特征；以及血管原型嵌入（VPEs），用于从多尺度特征图和血管掩膜中捕获细粒度的模态特异性血管特征。由于两者都需要在推理期间不可用的血管掩膜，并且需要鲁棒的模态感知血管特征表征，我们构建了一个按模态划分的血管数据库，并开发了两种参考图引导的表征学习方案，利用数据库中的样本而非真实掩膜来估计GPEs和VPEs。在19个数据集上的大量实验表明，ReG-SAM始终优于现有基线，甚至优于使用手动提示的方法，尤其是在具有挑战性的细薄血管上。
英文摘要
Vessel segmentation in medical images is essential for many clinical tasks, ranging from diagnosis to treatment planning. However, it remains challenging due to complex vascular morphology and diverse imaging conditions. Existing deep learning methods rarely aim at building a generalizable vessel segmentor across anatomies and modalities. While the Seg- ment Anything Model (SAM) has shown promise for med- ical image segmentation, its original design does not fully exploit vascular morphology and struggles with fine-grained vascular structures, leading to suboptimal performance. In this paper, we propose ReG-SAM, a SAM-based framework tailored to 2D vessel segmentation that leverages reference graph set for enhancing vascular representations. Specifically, we introduce two modality-aware representations derived from the reference masks: graph prompt embeddings (GPEs) that encode global spatial features from graphs, and vascu- lar prototype embeddings (VPEs) that capture fine-grained modality-specific vessel characteristics from multi-scale fea- ture maps and vascular masks. Since both require vascular masks that are unavailable during inference and require robust modality-aware vascular feature representations, we construct a modality-wise vascular database and develop two reference graph-guided representation learning schemes for estimating GPEs and VPEs using samples from the database rather than ground-truth masks. Extensive experiments across 19 datasets demonstrate that ReG-SAM consistently outperforms existing baselines, even those using manual prompts, particularly on challenging thin vessels.
