# DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences

arXiv: https://arxiv.org/abs/2306.12153 · HTML: https://mlsanity.com/paper/2306.12153/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 67 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 30 of 30)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [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)
- [ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2106.05095/) (2021)
- [Delving Deeper into Convolutional Networks for Learning Video Representations](https://mlsanity.com/paper/1511.06432/) (2015)
- [On the Challenges and Perspectives of Foundation Models for Medical Image Analysis](https://mlsanity.com/paper/2306.05705/) (2023)
- [On Regularized Losses for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1803.09569/) (2018)
- [Normalized Cut Loss for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1804.01346/) (2018)
- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision](https://mlsanity.com/paper/2203.02106/) (2022)
- [Semi-Supervised Video Salient Object Detection Using Pseudo-Labels](https://mlsanity.com/paper/1908.04051/) (2019)
- [CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging](https://mlsanity.com/paper/2010.07486/) (2020)
- [Deep Vessel Segmentation By Learning Graphical Connectivity](https://mlsanity.com/paper/1806.02279/) (2018)
- [Attention Guided Network for Retinal Image Segmentation](https://mlsanity.com/paper/1907.12930/) (2019)
- [RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network](https://mlsanity.com/paper/2101.00535/) (2021)
- [Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels](https://mlsanity.com/paper/2103.03451/) (2021)
- [Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures](https://mlsanity.com/paper/2208.08605/) (2022)
- [PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation](https://mlsanity.com/paper/2203.04568/) (2022)
- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.13393/) (2021)
- [autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients](https://mlsanity.com/paper/2010.01432/) (2020)
- [Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT](https://mlsanity.com/paper/2108.00831/) (2021)
- [BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation](https://mlsanity.com/paper/2104.03768/) (2021)
- [Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network](https://mlsanity.com/paper/2004.12776/) (2020)
- [Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation](https://mlsanity.com/paper/2207.11512/) (2022)

## Built on by (most cited first, 10 of 10)

- [DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentation](https://mlsanity.com/paper/2406.00341/) (2024)
- [SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation](https://mlsanity.com/paper/2503.16055/) (2025)
- [A Physics-Informed Loss Function for Boundary-Consistent and Robust Artery Segmentation in DSA Sequences](https://mlsanity.com/paper/2511.20501/) (2025)
- [Leptomeningeal Collateral Detection on DSA via Vessel-Graph Neural Networks](https://mlsanity.com/paper/2606.14828/) (2026)
- [Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentation](https://mlsanity.com/paper/2503.19589/) (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)
- [X-LMC: Cross-View Spatiotemporal Collateral Circulation Scoring from DSA](https://mlsanity.com/paper/2608.18986/) (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)

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.