Automatic segmentation of the intracranial artery (IA) in digital subtraction angiography (DSA) sequence is an essential step in diagnosing IA-related diseases and guiding neuro-interventional surgery. However, the lack of publicly available datasets has impeded research in this area. In this paper, we release DIAS, an IA segmentation dataset, consisting of 120 DSA sequences from intracranial interventional therapy. In addition to pixel-wise annotations, this dataset provides two types of scribble annotations for weakly supervised IA segmentation research. We present a comprehensive benchmark for evaluating the performance of this challenging dataset by utilizing fully-, weakly-, and semi-supervised learning approaches. Specifically, we propose a method that incorporates a dimensionality reduction module into a 2D/3D model to achieve vessel segmentation in DSA sequences. For weakly-supervised learning, we propose a scribble learning-based image segmentation framework, SSCR, which comprises scribble supervision and consistency regularization. Furthermore, we introduce a random patch-based self-training framework that utilizes unlabeled DSA sequences to improve segmentation performance. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications.

翻译：数字减影血管造影（DSA）序列中颅内动脉（IA）的自动分割是诊断IA相关疾病及指导神经介入手术的关键步骤。然而，公共数据集的匮乏阻碍了该领域的研究进展。本文发布IA分割数据集DIAS，包含来自颅内介入治疗的120个DSA序列。除像素级标注外，该数据集还提供两种涂鸦标注形式以支持弱监督IA分割研究。我们通过全监督、弱监督和半监督学习方法构建了综合基准数据集以评估该挑战性任务的性能。具体而言，我们提出将降维模块集成至2D/3D模型的方法以实现DSA序列中的血管分割。针对弱监督学习，我们提出基于涂鸦学习的图像分割框架SSCR，该框架包含涂鸦监督与一致性正则化机制。此外，我们引入基于随机补丁的自训练框架，利用未标注DSA序列提升分割性能。在DIAS数据集上的大量实验表明，这些方法可作为未来研究与临床应用的有效基线。