  Cerebrovascular diseases (CVDs) remain a leading cause of global disability
and mortality. Digital Subtraction Angiography (DSA) sequences, recognized as
the golden standard for diagnosing CVDs, can clearly visualize the dynamic flow
and reveal pathological conditions within the cerebrovasculature. Therefore,
precise segmentation of cerebral arteries (CAs) and classification between
their main trunks and branches are crucial for physicians to accurately
quantify diseases. However, achieving accurate CA segmentation in DSA sequences
remains a challenging task due to small vessels with low contrast, and
ambiguity between vessels and residual skull structures. Moreover, the lack of
publicly available datasets limits exploration in the field. In this paper, we
introduce a DSA Sequence-based Cerebral Artery segmentation dataset (DSCA), the
first publicly accessible dataset designed specifically for pixel-level
semantic segmentation of CAs. Additionally, we propose DSANet, a
spatio-temporal network for CA segmentation in DSA sequences. Unlike existing
DSA segmentation methods that focus only on a single frame, the proposed DSANet
introduces a separate temporal encoding branch to capture dynamic vessel
details across multiple frames. To enhance small vessel segmentation and
improve vessel connectivity, we design a novel TemporalFormer module to capture
global context and correlations among sequential frames. Furthermore, we
develop a Spatio-Temporal Fusion (STF) module to effectively integrate spatial
and temporal features from the encoder. Extensive experiments demonstrate that
DSANet outperforms other state-of-the-art methods in CA segmentation, achieving
a Dice of 0.9033.
