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

• URL: http://arxiv.org/abs/2306.12153v4
• Date: Thu, 13 Jun 2024 14:16:10 GMT
• Title: DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
• Authors: Wentao Liu, Tong Tian, Lemeng Wang, Weijin Xu, Lei Li, Haoyuan Li, Wenyi Zhao, Siyu Tian, Xipeng Pan, Huihua Yang, Feng Gao, Yiming Deng, Xin Yang, Ruisheng Su,
• Abstract summary: Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. We introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences.
• Score: 19.61593883367223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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