标题： 冠状动脉分割中视觉基础模型的释放：并行ViT-CNN编码和变分融合 显示英文标题

标题： Unleashing Vision Foundation Models for Coronary Artery Segmentation: Parallel ViT-CNN Encoding and Variational Fusion

摘要： 准确的冠状动脉分割对于计算机辅助诊断冠状动脉疾病（CAD）至关重要，但由于血管尺寸小、形态复杂以及与周围组织对比度低，仍然具有挑战性。 为解决这些挑战，我们提出了一种新的分割框架，通过并行编码架构利用视觉基础模型（VFMs）的强大功能。 具体来说，VFM中的视觉变压器（ViT）编码器通过激活最后两个ViT块并集成注意力引导增强（AGE）模块来捕捉全局结构特征，而卷积神经网络（CNN）编码器提取局部细节。 这些互补特征通过跨分支变分融合（CVF）模块进行自适应融合，该模块建模潜在分布并应用变分注意力以分配模态特定权重。 此外，我们引入了一个证据学习不确定性精炼（EUR）模块，该模块使用证据理论量化不确定性，并通过结合多尺度特征聚合和注意力机制来精炼不确定区域，进一步提高分割准确性。 在一项内部数据集和两个公共数据集上的广泛评估表明，所提出的框架显著优于最先进的方法，在准确的冠状动脉分割中表现出色，并展示了在多个数据集上的强大泛化能力。 代码可在 https://github.com/d1c2x3/CAseg 获取。 显示英文摘要

摘要： Accurate coronary artery segmentation is critical for computeraided diagnosis of coronary artery disease (CAD), yet it remains challenging due to the small size, complex morphology, and low contrast with surrounding tissues. To address these challenges, we propose a novel segmentation framework that leverages the power of vision foundation models (VFMs) through a parallel encoding architecture. Specifically, a vision transformer (ViT) encoder within the VFM captures global structural features, enhanced by the activation of the final two ViT blocks and the integration of an attention-guided enhancement (AGE) module, while a convolutional neural network (CNN) encoder extracts local details. These complementary features are adaptively fused using a cross-branch variational fusion (CVF) module, which models latent distributions and applies variational attention to assign modality-specific weights. Additionally, we introduce an evidential-learning uncertainty refinement (EUR) module, which quantifies uncertainty using evidence theory and refines uncertain regions by incorporating multi-scale feature aggregation and attention mechanisms, further enhancing segmentation accuracy. Extensive evaluations on one in-house and two public datasets demonstrate that the proposed framework significantly outperforms state-of-the-art methods, achieving superior performance in accurate coronary artery segmentation and showcasing strong generalization across multiple datasets. The code is available at https://github.com/d1c2x3/CAseg.