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%。