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.

通过引入最大规模的公共基准 BvEM，本研究致力于解决神经影像学领域的重大课题，在体积电子显微镜（VEM）图像中，专门设计用于皮质血管分割。本研究开发了一种名为 TriSAM 的零射的皮质血管分割方法，并基于三种哺乳动物（成年小鼠、猕猴和人类）的 VEM 图像体积构建了 BvEM 基准。实验结果表明，TriSAM 在三种物种的 BvEM 基准上取得了优越的性能。