DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentation

2024-06-01

arXiv.org

AI Summary

To address challenges in digital subtraction angiography (DSA) sequences—including low segmentation accuracy, difficulty distinguishing arterial trunks from branches, poor vessel-to-skull contrast, and the absence of publicly available datasets—this paper introduces DSCA, the first open-source DSA cerebral artery segmentation dataset. We further propose DSANet, a novel architecture featuring a TemporalFormer module to model global inter-frame temporal dependencies, and a spatiotemporal fusion module that deeply integrates spatial features with Transformer-based temporal representations—overcoming limitations of single-frame segmentation and substantially improving small-vessel and branch detection. Evaluated on DSCA, DSANet achieves a Dice score of 0.9033, outperforming state-of-the-art methods. It is the first method to enable high-precision, pixel-level segmentation of both arterial trunks and branches in dynamic DSA sequences.

Technology Category

• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Cerebrovascular diseases (CVDs) remain a leading cause of global disability and mortality. Digital Subtraction Angiography (DSA) sequences, recognized as the gold 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 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.

Problem

• Segment cerebral arteries in DSA sequences accurately.
• Classify main trunks and branches of cerebral arteries.
• Address challenges in small vessel and low contrast segmentation.

Innovation

• DSANet spatio-temporal network
• TemporalFormer module
• Spatio-Temporal Fusion module