In this paper, we address a significant gap in the field of neuroimaging by introducing the largest-to-date public benchmark, BvEM, designed specifically for cortical blood vessel segmentation in Volume Electron Microscopy (VEM) images. The intricate relationship between cerebral blood vessels and neural function underscores the vital role of vascular analysis in understanding brain health. While imaging techniques at macro and mesoscales have garnered substantial attention and resources, the microscale VEM imaging, capable of revealing intricate vascular details, has lacked the necessary benchmarking infrastructure. As researchers delve deeper into the microscale intricacies of cerebral vasculature, our BvEM benchmark represents a critical step toward unraveling the mysteries of neurovascular coupling and its impact on brain function and pathology. The BvEM dataset is based on VEM image volumes from three mammal species: adult mouse, macaque, and human. We standardized the resolution, addressed imaging variations, and meticulously annotated blood vessels through semi-automatic, manual, and quality control processes, ensuring high-quality 3D segmentation. Furthermore, we developed a zero-shot cortical blood vessel segmentation method named TriSAM, which leverages the powerful segmentation model SAM for 3D segmentation. To lift SAM from 2D segmentation to 3D volume segmentation, TriSAM employs a multi-seed tracking framework, leveraging the reliability of certain image planes for tracking while using others to identify potential turning points. This approach, consisting of Tri-Plane selection, SAM-based tracking, and recursive redirection, effectively achieves long-term 3D blood vessel segmentation without model training or fine-tuning. Experimental results show that TriSAM achieved superior performances on the BvEM benchmark across three species.

翻译：本文通过引入迄今为止最大的公开基准数据集BvEM，填补了神经影像领域的重要空白，该数据集专为体积电子显微镜（VEM）图像中的皮层血管分割而设计。脑血管与神经功能之间的复杂关系凸显了血管分析在理解脑健康中的关键作用。尽管宏观与介观尺度的成像技术已获得广泛关注与资源投入，但能揭示精细血管结构的微观尺度VEM成像却缺乏必要的基准基础设施。随着研究人员深入探索脑血管系统的微观复杂性，我们的BvEM基准数据集成为揭示神经血管耦合机制及其对脑功能与病理影响的关键一步。BvEM数据集基于三种哺乳动物（成年小鼠、猕猴和人类）的VEM图像体数据。我们统一了分辨率标准，解决了成像差异问题，并通过半自动、人工标注及质量管控流程精细标注血管，确保了高精度的三维分割。此外，我们开发了一种名为TriSAM的皮层血管零样本分割方法，该方法利用强大的分割模型SAM实现三维分割。为将SAM从二维分割提升至三维体分割，TriSAM采用多种子点追踪框架，利用特定图像平面的可靠性进行追踪，同时借助其他平面识别潜在转折点。该框架由三平面选择、基于SAM的追踪和递归重定向三个环节组成，可在无需模型训练或微调的前提下有效实现长程三维血管分割。实验结果表明，TriSAM在涵盖三个物种的BvEM基准数据集上均取得了优异性能。