  While imaging techniques at macro and mesoscales have garnered substantial
attention and resources, microscale Volume Electron Microscopy (vEM) imaging,
capable of revealing intricate vascular details, has lacked the necessary
benchmarking infrastructure. In this paper, we address a significant gap in
this field of neuroimaging by introducing the first-in-class public benchmark,
BvEM, designed specifically for cortical blood vessel segmentation in vEM
images. Our BvEM benchmark is based on vEM image volumes from three mammals:
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 extend SAM from 2D 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 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. Our dataset, code, and model are available online at
\url{https://jia-wan.github.io/bvem}.
