  The automated segmentation of Intracranial Arteries (IA) in Digital
Subtraction Angiography (DSA) plays a crucial role in the quantification of
vascular morphology, significantly contributing to computer-assisted stroke
research and clinical practice. Current research primarily focuses on the
segmentation of single-frame DSA using proprietary datasets. However, these
methods face challenges due to the inherent limitation of single-frame DSA,
which only partially displays vascular contrast, thereby hindering accurate
vascular structure representation. In this work, we introduce DIAS, a dataset
specifically developed for IA segmentation in DSA sequences. We establish a
comprehensive benchmark for evaluating DIAS, covering full, weak, and
semi-supervised segmentation methods. Specifically, we propose the vessel
sequence segmentation network, in which the sequence feature extraction module
effectively captures spatiotemporal representations of intravascular contrast,
achieving intracranial artery segmentation in 2D+Time DSA sequences. For
weakly-supervised IA segmentation, we propose a novel scribble learning-based
image segmentation framework, which, under the guidance of scribble labels,
employs cross pseudo-supervision and consistency regularization to improve the
performance of the segmentation network. Furthermore, we introduce the random
patch-based self-training framework, aimed at alleviating the performance
constraints encountered in IA segmentation due to the limited availability of
annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate
the effectiveness of these methods as potential baselines for future research
and clinical applications. The dataset and code are publicly available at
https://doi.org/10.5281/zenodo.11396520 and https://github.com/lseventeen/DIAS.
