Cerebrovascular disease is one of the major diseases facing the world today.
Automatic segmentation of intracranial artery (IA) in digital subtraction
angiography (DSA) sequences is an important step in the diagnosis of vascular
related diseases and in guiding neurointerventional procedures. While, a single
image can only show part of the IA within the contrast medium according to the
imaging principle of DSA technology. Therefore, 2D DSA segmentation methods are
unable to capture the complete IA information and treatment of cerebrovascular
diseases. We propose A timing sequence image segmentation network with U-shape,
called TSI-Net, which incorporates a bi-directional ConvGRU module (BCM) in the
encoder. The network incorporates a bi-directional ConvGRU module (BCM) in the
encoder, which can input variable-length DSA sequences, retain past and future
information, segment them into 2D images. In addition, we introduce a sensitive
detail branch (SDB) at the end for supervising fine vessels. Experimented on
the DSA sequence dataset DIAS, the method performs significantly better than
state-of-the-art networks in recent years. In particular, it achieves a Sen
evaluation metric of 0.797, which is a 3% improvement compared to other
methods.

我们提出了一种时间序列图像分割网络 TSI-Net，它采用了一个带有双向 ConvGRU 模块的 U 形结构，能够输入可变长度的 DSA 序列，保留过去和未来信息，并将其分割成 2D 图像。此网络在最近几年的动态支架血管内治疗中的表现显著优于其他方法，尤其是在敏感细节方面的性能提高了 3%。

TSI-Net：数字减影血管造影中颅内动脉分割的时间序列图像分割网络

TSI-Net: A Timing Sequence Image Segmentation Network for Intracranial  Artery Segmentation in Digital Subtraction Angiography

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。