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.

本研究旨在研究数字减影（DSA）成像中颅内动脉（IA）的分割及其在 IA 相关疾病的诊断与神经介入手术中的应用，并在此基础上提出了将维度约简与二维 / 三维模型相结合的方法，提出了一种基于涂鸦学习的图像分割框架，SSCR，并介绍了一种基于随机裁剪的自学习框架，旨在提高颅内动脉分割的性能。