  Angiography is widely used to detect, diagnose, and treat cerebrovascular
diseases. While numerous techniques have been proposed to segment the vascular
network from different imaging modalities, deep learning (DL) has emerged as a
promising approach. However, existing DL methods often depend on proprietary
datasets and extensive manual annotation. Moreover, the availability of
pre-trained networks specifically for medical domains and 3D volumes is
limited. To overcome these challenges, we propose a few-shot learning approach
called VesselShot for cerebrovascular segmentation. VesselShot leverages
knowledge from a few annotated support images and mitigates the scarcity of
labeled data and the need for extensive annotation in cerebral blood vessel
segmentation. We evaluated the performance of VesselShot using the publicly
available TubeTK dataset for the segmentation task, achieving a mean Dice
coefficient (DC) of 0.62(0.03).
