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

arXiv: https://arxiv.org/abs/2406.00341 · HTML: https://mlsanity.com/paper/2406.00341/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging](https://mlsanity.com/paper/2010.07486/) (2020)
- [Attention Guided Network for Retinal Image Segmentation](https://mlsanity.com/paper/1907.12930/) (2019)
- [Dense Dilated Network with Probability Regularized Walk for Vessel Detection](https://mlsanity.com/paper/1910.12010/) (2019)
- [DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences](https://mlsanity.com/paper/2306.12153/) (2023)
- [autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients](https://mlsanity.com/paper/2010.01432/) (2020)
- [CAVE: Cerebral Artery-Vein Segmentation in Digital Subtraction Angiography](https://mlsanity.com/paper/2208.02355/) (2022)

## Built on by (most cited first, 8 of 8)

- [A Physics-Informed Loss Function for Boundary-Consistent and Robust Artery Segmentation in DSA Sequences](https://mlsanity.com/paper/2511.20501/) (2025)
- [OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA](https://mlsanity.com/paper/2508.14286/) (2025)
- [Semantically Conditioned Diffusion Models for Cerebral DSA Synthesis](https://mlsanity.com/paper/2602.11703/) (2026)
- [Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models](https://mlsanity.com/paper/2605.21861/) (2026)
- [GeoPose: Patient-agnostic CTA-to-DSA registration through projection-space calibration](https://mlsanity.com/paper/2608.16600/) (2026)
- [UI-VISA: U-Net Initialized Vascular Image Segmentation Architecture](https://mlsanity.com/paper/2609.01598/) (2026)
- [ConPro: Contrast Projection Pretraining for Label-Efficient Vessel Segmentation in DSA Sequences](https://mlsanity.com/paper/2609.30043/) (2026)
- [ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation](https://mlsanity.com/paper/2609.31160/) (2026)

## Abstract (arXiv)

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.